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The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. 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The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. 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and future work</a> </div> </li> <li class="section-jump-link head-1" link-destination="225532513"> <div class="section-jump-link__link-wrap"> <a class="js-jumplink scrollTo" href="#225532513">5 Summary of materials and methodologies</a> </div> </li> <li class="section-jump-link head-1" link-destination="225532517"> <div class="section-jump-link__link-wrap"> <a class="js-jumplink scrollTo" href="#225532517">Funding</a> </div> </li> <li class="section-jump-link head-1 backReferenceLink" link-destination="225532530"> <div class="section-jump-link__link-wrap"> <a class="js-jumplink scrollTo" href="#225532530">References</a> </div> </li> </ul> </div> </div> <div class="widget widget-ArticleNavLinks widget-instance-OUP_ArticleNavLinks_Article"> <ul class="inline-list"> <li class="prev arrow"> <a href="/bib/article/22/1/232/5700591">< Previous</a> </li> <li class="next arrow"> <a href="/bib/article/22/1/270/5685758">Next ></a> </li> </ul> </div> </div> </div> </div> <div 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Medicine and Bioinformatics</div>, University of Michigan, Ann Arbor, MI, 48109, USA</div> </div> <div class="info-author-correspondence"> <div content-id="cor1">Corresponding author: Maryam Bagherian and Kayvan Najarian, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA. 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for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/bib/search-results?f_Authors=Kayvan+Najarian" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Najarian K">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Najarian Kayvan%22">Google Scholar</a> </div> </div> </span> </span> </div> </div> <div class="pub-history-wrap clearfix js-history-dropdown-wrap"> <div class="pub-history-row clearfix"> <div class="ww-citation-primary"><em>Briefings in Bioinformatics</em>, Volume 22, Issue 1, January 2021, Pages 247–269, <a href='https://doi.org/10.1093/bib/bbz157'>https://doi.org/10.1093/bib/bbz157</a></div> </div> <div class="pub-history-row clearfix"> <div class="ww-citation-date-wrap"> <div class="citation-label">Published:</div> <div 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<div id="ContentTab" class="content active"> <div class="widget widget-ArticleFulltext widget-instance-OUP_Article_FullText_Widget"> <div class="module-widget"> <div class="widget-items" data-widgetname="ArticleFulltext"> <h2 scrollto-destination=225532389 id="225532389" class="abstract-title js-splitscreen-abstract-title" >Abstract</h2> <section class="abstract"><p class="chapter-para">The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.</p></section> <div class="article-metadata-panel clearfix at-ArticleMetadata"></div> <div class="kwd-group"><a class="kwd-part kwd-main" href="javascript:;" data-keyword=""Machine learning"">Machine learning</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword=""drug–target interaction prediction"">drug–target interaction prediction</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword=""DTI software"">DTI software</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword=""DTI database"">DTI database</a></div> <h2 scrollto-destination=225532391 id="225532391" class="section-title js-splitscreen-section-title" data-legacy-id=sec1>1 Introduction</h2> <p class="chapter-para">In recent years, pharmaceutical scientists have been highly focused on novel drug development strategies that rely on knowledge about existing drugs [<span class="xrefLink" id="jumplink-ref1 ref2 ref3 ref4 ref5"></span><a href="javascript:;" reveal-id="ref1 ref2 ref3 ref4 ref5" data-open="ref1 ref2 ref3 ref4 ref5" class="link link-ref link-reveal xref-bibr">1–5</a>]. Indeed, the difficulty of the drug discovery task lies in the rarity of existing drug–gene interactions [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>], and a major risk is in unexpected/unintended interaction of drugs with off-target proteins, i.e. side effects [<span class="xrefLink" id="jumplink-ref7 ref8 ref9"></span><a href="javascript:;" reveal-id="ref7 ref8 ref9" data-open="ref7 ref8 ref9" class="link link-ref link-reveal xref-bibr">7–9</a>]. While most of these side effects are undesired and harmful, occasionally they lead to interesting therapeutic discoveries. For instance, minoxidil was primarily developed to treat ulcers, and Sildenafil (Viagra) was developed to treat angina; however, they are currently used for treatment of hair loss and erectile dysfunction, respectively. As such, novel drug development strategies are currently the principle focus of many pharmacologists. It has been reported that several terms such as drug repositioning, drug repurposing, drug reprofiling, drug redirecting, drug rediscovery and drug delivery have been used in the literature to describe these novel drug development strategies [<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr">3</a>]. While various definitions have been used for these terms [<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr">3</a>], drug repositioning usually refers to the studies that reinvestigate existing drugs that failed approval for new therapeutic indications [<span class="xrefLink" id="jumplink-ref10"></span><a href="javascript:;" reveal-id="ref10" data-open="ref10" class="link link-ref link-reveal xref-bibr">10</a>], while drug repurposing suggests the application of already approved drugs and compounds to treat a different disease [<span class="xrefLink" id="jumplink-ref11"></span><a href="javascript:;" reveal-id="ref11" data-open="ref11" class="link link-ref link-reveal xref-bibr">11</a>, <span class="xrefLink" id="jumplink-ref12"></span><a href="javascript:;" reveal-id="ref12" data-open="ref12" class="link link-ref link-reveal xref-bibr">12</a>].</p> <a id="225532393" scrollto-destination="225532393"></a> <div data-id="f1" data-content-id="f1" class="fig fig-section js-fig-section" swap-content-for-modal="true"><div class="graphic-wrap"><img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/m_bbz157f1.jpeg?Expires=1734462476&Signature=v790t7SE6ca55Xus9UdnOcls79fhnOwDGXW0vcw0-CaA5cYFPsn8c3kIO~nz1lfbibvC6xLHXm-1JrnFq4UzBfDKM5w3CBp-UO~W0FcJvnSBzcKt~EHRAJMYqcoCWsYziudcki2~aqGaHDQpHM-0i8LaGHAtC2KmCNTf5lP2t9CAjet76flKMR1sc1cCvdrvBUDuLQ-tvcklbehj8sKBftttCyJIGFBTZDFzt2NIOH4HTbbyKZiweRAgMgIJ8ESbblF5Cbm~SzvUwdAyeRRax-vBvT30NMWEjn9XQ-O6c1cOzfSFRGx4Ux5BHTZAAjrr3dPi5W8fPsILZMjdRZliJg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="An overview of the present work." data-path-from-xml="bbz157f1.tif" /><div class="graphic-bottom"><div class="label fig-label" id="label-225532393"><strong>Figure 1</strong></div><div class="caption fig-caption"><p class="chapter-para">An overview of the present work.</p></div><div class="ajax-articleAbstract-exclude-regex fig-orig original-slide figure-button-wrap"><a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" role="button" aria-describedby="label-225532393" href="/view-large/figure/225532393/bbz157f1.tif" data-path-from-xml="bbz157f1.tif" target="_blank">Open in new tab</a><a class="download-slide" role="button" aria-describedby="label-225532393" data-section="225532393" href="/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/bbz157f1.jpeg?Expires=1734462476&Signature=QkoEUoBy38dmKKDB9bP-O2fYSNIf1UnhxHagVKRLLTZl9f9bgjSduMDuWb09Jj6~EWFpkjpg~ZCzxwe7XlPWkbeT~DKhyAMG0MMbWfcmGh5DcEqSsQ96qg6qsPciu5IYdGlm~IPDNsmnivDAifRmgNAgxqz~IY-0TRQvNPyvujar58VIe4vQMlkfxo2Z46GAJftwoZaRMOyUsA-D6Q3e302B3c6Izqc-nFqQaZmKUFWzNq4EWAACuTom3gtZ3EeNzz~4ooPMhM9NByQtufB5wXNxoCFOgG01T1DwqyVZE65CzO7QL7PAAICU91akKsKRgXjHiv6LFkFjZGKdSVMMmA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=225532393&ar=5681786&xsltPath=~/UI/app/XSLT&imagename=&siteId=5143" data-path-from-xml="bbz157f1.tif">Download slide</a></div></div></div></div><p class="chapter-para">A major step in the drug discovery process is to identify interactions between drugs and targets (e.g. genes), which can be reliably performed by <em>in vitro</em> experiments. In order to reduce temporal and monetary costs,<em>in silico</em> approaches are gaining more attention [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]. As such, instead of an exhausting <em>in vitro</em> search, virtual screening is initially performed and possible candidates are then experimentally verified [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]. Generally, there are two principle approaches for <em>in silico</em> prediction of drug–target interaction (DTI, also refered to as compound–protein interactions): docking simulations and machine learning methods [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]. In docking simulations, the 3D structure of drug molecules and targets are considered and potential binding sites are identified. While biologically well accepted, the docking simulation process is time-consuming [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]. Additionally, this process cannot be applied if the 3D structure of the protein is unknown [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>]. For instance, for a class of proteins called G-protein-coupled receptors (GPCR), very few structures have been crystallized (orphan GPCR) [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref15"></span><a href="javascript:;" reveal-id="ref15" data-open="ref15" class="link link-ref link-reveal xref-bibr">15</a>], so docking simulations cannot be applied. To tackle this issue, chemogenomics was introduced as a way to aim at mining the entire chemical space for interaction with the biological space (also refered to as genomic space), instead of considering each protein target independently from other proteins [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref16"></span><a href="javascript:;" reveal-id="ref16" data-open="ref16" class="link link-ref link-reveal xref-bibr">16</a>, <span class="xrefLink" id="jumplink-ref17"></span><a href="javascript:;" reveal-id="ref17" data-open="ref17" class="link link-ref link-reveal xref-bibr">17</a>].</p><p class="chapter-para">The aim of chemogenomics research is to relate this chemical space of possible compounds with the genomic space in order to identify potentially useful compounds such as imaging probes and drug leads [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>]. Chemogenomics approaches are usually categorized as ligand based, target based and target–ligand [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref17"></span><a href="javascript:;" reveal-id="ref17" data-open="ref17" class="link link-ref link-reveal xref-bibr">17</a>], all of which are based on similarities between members proteins and targets. In fact, this salient similarity-based point of view of chemogenomics allowed the machine learning approaches to be suitable for prediction of DTIs. In machine learning methods [<span class="xrefLink" id="jumplink-ref18"></span><a href="javascript:;" reveal-id="ref18" data-open="ref18" class="link link-ref link-reveal xref-bibr">18</a>], knowledge about drugs, targets and already confirmed DTIs are translated into features that are used to train a predictive model, which in turn is used to predict interactions between new drugs and/or new targets. The main assumption of these studies is that if drug <span class="inline-formula no-formula-id">|$d$|</span> is interacting with protein <span class="inline-formula no-formula-id">|$p$|⁠</span>, then (i) drug compounds similar to <span class="inline-formula no-formula-id">|$d$|</span> are likely to interact with protein <span class="inline-formula no-formula-id">|$p$|⁠</span>, (ii) proteins similar to <span class="inline-formula no-formula-id">|$p$|</span> are likely to interact with drug <span class="inline-formula no-formula-id">|$d$|</span> and (iii) drug compounds similar to <span class="inline-formula no-formula-id">|$d$|</span> are likely to interact with proteins similar to <span class="inline-formula no-formula-id">|$p$|⁠</span>. The similarities between drug compounds and protein sequences are usually measured by kernels specifically designed for this purpose [<span class="xrefLink" id="jumplink-ref19"></span><a href="javascript:;" reveal-id="ref19" data-open="ref19" class="link link-ref link-reveal xref-bibr">19</a>]. In practice, based on the availability of knowledge about interacting drug compounds and target proteins, the DTI prediction problem can be categorized into four classes: (i) known drug versus known target, (ii) known drug versus new target candidate, (iii) new drug candidate versus known target and (iv) new drug candidate versus new target candidate. While the ultimate goal of the machine learning methods is interaction prediction for new drug and target candidates, most of the methods in the literature are limited to the 1st three classes.</p><p class="chapter-para">In this paper, the state of the art methods, which used machine learning methods for prediction of DTIs, are reviewed. The following studies were excluded:</p><ul class="list-simple"><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that do not use machine learning methods for prediction or (e.g. [<span class="xrefLink" id="jumplink-ref20 ref21 ref22 ref23 ref24 ref25"></span><a href="javascript:;" reveal-id="ref20 ref21 ref22 ref23 ref24 ref25" data-open="ref20 ref21 ref22 ref23 ref24 ref25" class="link link-ref link-reveal xref-bibr">20–25</a>]).</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that focus on bioactivity (quantitative structure–activity relationship (SAR), proteochemometric) relationships (e.g. [<span class="xrefLink" id="jumplink-ref26 ref27 ref28 ref29 ref30 ref31 ref32"></span><a href="javascript:;" reveal-id="ref26 ref27 ref28 ref29 ref30 ref31 ref32" data-open="ref26 ref27 ref28 ref29 ref30 ref31 ref32" class="link link-ref link-reveal xref-bibr">26–32</a>]).</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that rely on 3D structures of targets (e.g. [<span class="xrefLink" id="jumplink-ref33 ref34 ref35 ref36"></span><a href="javascript:;" reveal-id="ref33 ref34 ref35 ref36" data-open="ref33 ref34 ref35 ref36" class="link link-ref link-reveal xref-bibr">33–36</a>]).</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that consider only the genomic space or chemical space (e.g. [<span class="xrefLink" id="jumplink-ref4"></span><a href="javascript:;" reveal-id="ref4" data-open="ref4" class="link link-ref link-reveal xref-bibr">4</a>, <span class="xrefLink" id="jumplink-ref37 ref38 ref39 ref40 ref41 ref42 ref43 ref44 ref45 ref46 ref47 ref48 ref49 ref50 ref51 ref52"></span><a href="javascript:;" reveal-id="ref37 ref38 ref39 ref40 ref41 ref42 ref43 ref44 ref45 ref46 ref47 ref48 ref49 ref50 ref51 ref52" data-open="ref37 ref38 ref39 ref40 ref41 ref42 ref43 ref44 ref45 ref46 ref47 ref48 ref49 ref50 ref51 ref52" class="link link-ref link-reveal xref-bibr">37–52</a>]).</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that focus on gene expression for drug response (e.g. [<span class="xrefLink" id="jumplink-ref53 ref54 ref55 ref56 ref57 ref58"></span><a href="javascript:;" reveal-id="ref53 ref54 ref55 ref56 ref57 ref58" data-open="ref53 ref54 ref55 ref56 ref57 ref58" class="link link-ref link-reveal xref-bibr">53–58</a>]).</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that only use side effect similarities or only predict side effects (e.g. [<span class="xrefLink" id="jumplink-ref59 ref60 ref61 ref62 ref63"></span><a href="javascript:;" reveal-id="ref59 ref60 ref61 ref62 ref63" data-open="ref59 ref60 ref61 ref62 ref63" class="link link-ref link-reveal xref-bibr">59–63</a>]).</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that use disease–gene associations (e.g. [<span class="xrefLink" id="jumplink-ref64 ref65 ref66 ref67"></span><a href="javascript:;" reveal-id="ref64 ref65 ref66 ref67" data-open="ref64 ref65 ref66 ref67" class="link link-ref link-reveal xref-bibr">64–67</a>]).</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that focus on drug–drug interactions or protein–protein interactions (PPI) (e.g. [<span class="xrefLink" id="jumplink-ref68 ref69 ref70 ref71 ref72"></span><a href="javascript:;" reveal-id="ref68 ref69 ref70 ref71 ref72" data-open="ref68 ref69 ref70 ref71 ref72" class="link link-ref link-reveal xref-bibr">68–72</a>])</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> studies that use biomedical documents from which information is extracted by text mining techniques (e.g. [<span class="xrefLink" id="jumplink-ref73"></span><a href="javascript:;" reveal-id="ref73" data-open="ref73" class="link link-ref link-reveal xref-bibr">73</a>]).</p></li></ul><p class="chapter-para">It is worth mentioning that the machine learning methods used in DTI prediction can be thought of as a broader problem of ‘link predictions’ in complex networks [<span class="xrefLink" id="jumplink-ref74"></span><a href="javascript:;" reveal-id="ref74" data-open="ref74" class="link link-ref link-reveal xref-bibr">74</a>]. A section is dedicated to summarize the databases used in these studies as well. An overview of the paper is illustrated in Figure <span class="xrefLink" id="jumplink-f1"></span><a href="javascript:;" data-modal-source-id="f1" class="link xref-fig">1</a>.</p> <h2 scrollto-destination=225532399 id="225532399" class="section-title js-splitscreen-section-title" data-legacy-id=sec2>2 Machine learning methods used in DTI prediction</h2> <p class="chapter-para">Although all the DTI prediction frameworks that uses machine learning are summarized in this manuscript, recent methods that use matrix factorization algorithms have outperformed other methods in terms of efficiency. These methods take advantage of the recommender system approaches [<span class="xrefLink" id="jumplink-ref75"></span><a href="javascript:;" reveal-id="ref75" data-open="ref75" class="link link-ref link-reveal xref-bibr">75</a>, <span class="xrefLink" id="jumplink-ref76"></span><a href="javascript:;" reveal-id="ref76" data-open="ref76" class="link link-ref link-reveal xref-bibr">76</a>], while using both chemical and genomic information is optimal for the DTI prediction problem. This problem is very similar to the famous Netflix challenge [<span class="xrefLink" id="jumplink-ref77"></span><a href="javascript:;" reveal-id="ref77" data-open="ref77" class="link link-ref link-reveal xref-bibr">77</a>].</p> <a id="225532401" scrollto-destination="225532401"></a> <div data-id="f2" data-content-id="f2" class="fig fig-section js-fig-section" swap-content-for-modal="true"><div class="graphic-wrap"><img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/m_bbz157f2.jpeg?Expires=1734462476&Signature=Ya6301jGjhQHeN6RXA9e4nZPJbzWiGyRjHgjFHjcTX52YQHUAj12EPk0leJe1V6nG~aaX-C~Z7GPB7oK7AZF2NKJEfq04gwNoELxDS4Ww4r8OdfLuYISdI9nQsyNvvd-JLNwZhTz~fm0ZR5l0i8ITDDVDHbsNqyWkSelt74LyPn-EkaxokNeqhpBQ75RpkQboxCr7PmsTZ89ZR8rSXJM38V0zPmHhaGPOzfC~ee9RrIFkG7A9uQWPNGHHjHyEe5z4FkxZiLxjVr6qca0qO2~u~CkpuQuV86mP~ctZY3QL75zIekoEAHxc7BQKVvc6ttKYdSpnnwsZEXSkzX9SNaFAw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="Machine learning methods used in DTI prediction can be categorized into six main branches. A short description of each group of methods are provided is Section 2. Here the machine learning methods are classified into similarity/distance based methods where itself consists of three subgroups. All approaches that employ kernels, trees, boosted methods, random and rotation forrest, support vector machines, etc. are listed in feature-based group. Deep learning, matrix factorization and network based methods from the other three groups. Any combination of the methods listed above is considered in the category of hybrid methods." data-path-from-xml="bbz157f2.tif" /><div class="graphic-bottom"><div class="label fig-label" id="label-225532401"><strong>Figure 2</strong></div><div class="caption fig-caption"><p class="chapter-para">Machine learning methods used in DTI prediction can be categorized into six main branches. A short description of each group of methods are provided is Section <span class="xrefLink" id="jumplink-sec2"></span><a href="#sec2" class="sectionLink xref-sec js-xref-sec">2</a>. Here the machine learning methods are classified into similarity/distance based methods where itself consists of three subgroups. All approaches that employ kernels, trees, boosted methods, random and rotation forrest, support vector machines, etc. are listed in feature-based group. Deep learning, matrix factorization and network based methods from the other three groups. Any combination of the methods listed above is considered in the category of hybrid methods.</p></div><div class="ajax-articleAbstract-exclude-regex fig-orig original-slide figure-button-wrap"><a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" role="button" aria-describedby="label-225532401" href="/view-large/figure/225532401/bbz157f2.tif" data-path-from-xml="bbz157f2.tif" target="_blank">Open in new tab</a><a class="download-slide" role="button" aria-describedby="label-225532401" data-section="225532401" href="/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/bbz157f2.jpeg?Expires=1734462476&Signature=jCuKMNTMDsvXbejwfCefb~U80Be7sUZ-RaWoR357RfIG4DEsU487aBBbgFY6FYs-cvGan~nUsTWU6IuY3qXUIZCC5WN2e~fdF4kZXFGo6Irfrv4c963U91mVLUT0lDC3rzC81qryZcDExbL4k3keemIDeVy3jzxAQXNCZ3SdS7zfM2uybICnlIYXMT9JVDWW-gW7UldYPNrsOhgY6OFnkbDNDfZYTFaR26JVrz46b2B816tZ0yC3JYMatkg04TeXtkib1HVgnl8~MO2ES-VUpMW4LzoLECZepfad~KRDI9PKL7n8gzbrMGhoY7fhJFdnjpm3xZiCW7hwyrnfQeW7hw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=225532401&ar=5681786&xsltPath=~/UI/app/XSLT&imagename=&siteId=5143" data-path-from-xml="bbz157f2.tif">Download slide</a></div></div></div></div><p class="chapter-para">Machine learning methods used in DTI prediction date back to an early work in pharmacological DTI prediction [<span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>]. While the focus of their work was not specifically ‘drug discovery’, they aimed at finding a ranked list of molecule ligands that bind with each orphan GPCR where due to lack of crystallized 3D structures, docking simulation could not be used [<span class="xrefLink" id="jumplink-ref15"></span><a href="javascript:;" reveal-id="ref15" data-open="ref15" class="link link-ref link-reveal xref-bibr">15</a>]. Here, the machine learning approaches have been categorized into six groups (Figure <span class="xrefLink" id="jumplink-sec2"></span><a href="#sec2" class="sectionLink xref-sec js-xref-sec">2</a>). In the coming section, a description of each category along with a list of methods for each is provided. Moreover, advantages and disadvantages of each group of methods are briefly discussed.</p> <h3 scrollto-destination=225532403 id="225532403" class="section-title js-splitscreen-section-title" data-legacy-id=sec2a>2.1 Previous review papers</h3> <p class="chapter-para">There have been few reviews on DTI prediction with various emphases [<span class="xrefLink" id="jumplink-ref79 ref80 ref81 ref82 ref83"></span><a href="javascript:;" reveal-id="ref79 ref80 ref81 ref82 ref83" data-open="ref79 ref80 ref81 ref82 ref83" class="link link-ref link-reveal xref-bibr">79–83</a>]; however, none of these studies had a machine learning focus. For previous reviews on machine learning methods for DTI prediction, please see [<span class="xrefLink" id="jumplink-ref84 ref85 ref86 ref87 ref88 ref89 ref90 ref91 ref92 ref93 ref94"></span><a href="javascript:;" reveal-id="ref84 ref85 ref86 ref87 ref88 ref89 ref90 ref91 ref92 ref93 ref94" data-open="ref84 ref85 ref86 ref87 ref88 ref89 ref90 ref91 ref92 ref93 ref94" class="link link-ref link-reveal xref-bibr">84–94</a>]. In particular, [<span class="xrefLink" id="jumplink-ref84"></span><a href="javascript:;" reveal-id="ref84" data-open="ref84" class="link link-ref link-reveal xref-bibr">84</a>] is a brief review of similarity-based machine learning methods used for DTI prediction. As reported in this work, similarity-based approaches have four advantages: (i) the ydo not need feature extraction and feature selection, (ii) similarity measure kernels for both drugs and genes have been fully studied before, (iii) they can be easily incorporated with kernel-based learning methods such as support vector machine (SVM), (iv) they can be used to connect chemical space and the genomic space. In [<span class="xrefLink" id="jumplink-ref85"></span><a href="javascript:;" reveal-id="ref85" data-open="ref85" class="link link-ref link-reveal xref-bibr">85</a>], the focus of the review is on the methods that use both drug chemical structure and target protein sequence to predict DTIs. Mousavian <em>et al.</em> [<span class="xrefLink" id="jumplink-ref90"></span><a href="javascript:;" reveal-id="ref90" data-open="ref90" class="link link-ref link-reveal xref-bibr">90</a>] reviewed machine learning-based methods from supervised and semi-supervised perspectives. Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref91"></span><a href="javascript:;" reveal-id="ref91" data-open="ref91" class="link link-ref link-reveal xref-bibr">91</a>] reviewed the well-known databases, web servers and computational models used for DTI prediction. In this paper, computational approaches are divided into network-based methods and machine learning-based methods. Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref92"></span><a href="javascript:;" reveal-id="ref92" data-open="ref92" class="link link-ref link-reveal xref-bibr">92</a>] provided an ‘empirical’ overview on chemogenomic DTI prediction methods and the databases used. In their work, the chemogenomic methodologies are separated into five models: neighborhood models, bipartite local models, network diffusion models, matrix factorization models and feature-based classification models. Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref87"></span><a href="javascript:;" reveal-id="ref87" data-open="ref87" class="link link-ref link-reveal xref-bibr">87</a>] reviewed the machine learning methods and databases that used chemogenomic approaches of DTI prediction. As such, based on the way negative samples are handled, chemogenomic approaches are divided into two categories: (i) supervised learning methods such as similarity-based and feature-based methods, (ii) semi-supervised learning methods. Kurgan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref88"></span><a href="javascript:;" reveal-id="ref88" data-open="ref88" class="link link-ref link-reveal xref-bibr">88</a>] wrote one of the most comprehensive surveys of DTI predictions before April 2018. Sachdev <em>et al.</em> [<span class="xrefLink" id="jumplink-ref93"></span><a href="javascript:;" reveal-id="ref93" data-open="ref93" class="link link-ref link-reveal xref-bibr">93</a>] reviewed feature-based chemogenomic approaches (excluding similarity-based chemogenomic approaches) used for DTI prediction. In this survey, feature-based methods are categorized as: (i) SVM-based methods, (ii) ensemble-based methods (methods that employ decision tree or random forest) and (iii) miscellaneous techniques (neither SVM-based nor ensemble-based). Sercinoglu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref94"></span><a href="javascript:;" reveal-id="ref94" data-open="ref94" class="link link-ref link-reveal xref-bibr">94</a>] reviewed all the available databases for drug repurposing.</p> <h3 scrollto-destination=225532405 id="225532405" class="section-title js-splitscreen-section-title" data-legacy-id=sec2b>2.2 Similarity/distance-based methods</h3> <p class="chapter-para">The most popular group of methods used for DTI prediction incorporate drug–drug and target–target similarity measures through similarity or distance functions that are utilized to perform the prediction. These methods have been proposed and employed by several authors, mainly [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref95 ref96 ref97 ref98 ref99 ref100 ref101 ref102 ref103 ref104 ref105 ref106 ref107 ref108 ref109"></span><a href="javascript:;" reveal-id="ref95 ref96 ref97 ref98 ref99 ref100 ref101 ref102 ref103 ref104 ref105 ref106 ref107 ref108 ref109" data-open="ref95 ref96 ref97 ref98 ref99 ref100 ref101 ref102 ref103 ref104 ref105 ref106 ref107 ref108 ref109" class="link link-ref link-reveal xref-bibr">95–109</a>].</p><div class="
 block-child-p
 ">Generally, the methods consist of a similarity score scheme for either drug–drug, target–target or drug–target associations based on a known pair of drug–drug and target–target similarity measures. Similarily, the similarity measure could be obtained by a distance function that defines how similar (or here ‘close’) a new drug is with respect to the known pairs. There are several ways to define the ‘nearness’ through a distance function for nearest neighbor (NN) algorithms [<span class="xrefLink" id="jumplink-ref96"></span><a href="javascript:;" reveal-id="ref96" data-open="ref96" class="link link-ref link-reveal xref-bibr">96</a>, <span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>] among which the Euclidean distance is well known. For instance, authors in [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>] employed the following definition for the NN algorithm; assuming two vector spaces (aka sample spaces) <span class="inline-formula no-formula-id">|$V_1$|</span> and <span class="inline-formula no-formula-id">|$V_2$|⁠</span>, with the same dimension, the distance (nearness) of the two samples is denoted by <span class="inline-formula no-formula-id">|$\mathbf D(V_1,V_2)$|⁠</span>, where <div class="formula-wrap"><div class="disp-formula" id="jumplink-" content-id=""><div class="tex-math display-math">$$\begin{equation*} \mathbf D(V_1,V_2)= 1- \frac{V_1\cdot V_2}{||V_1||\,||V_2||}, \end{equation*}$$</div></div></div>where <span class="inline-formula no-formula-id">|$(\,\cdot \,)$|</span> and <span class="inline-formula no-formula-id">|$||\cdot ||$|</span> denote the inner product and the Euclidean norm, respectively. One could easily verify that <span class="inline-formula no-formula-id">|$\mathbf{D}$|</span> is indeed a distance function satisfying the definition of the distance.</div><p class="chapter-para">In addition to the above, the similarity/distance function could be also defined based on the pharmacological similarity of drugs and genomic similarity of protein sequences as well as the topological properties of a multipartite network of the existing drugs and protein targets [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>, <span class="xrefLink" id="jumplink-ref110"></span><a href="javascript:;" reveal-id="ref110" data-open="ref110" class="link link-ref link-reveal xref-bibr">110</a>]. To this end, authors in [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>] defined five drug–drug similarity measures as chemical based, ligand based, expression based, side effect based and annotation based. The main disadvantage of this group of methods lies in the fact that only a small number of drugs and their interactions are known while there exists copious unlabeled data among the datasets (see Section <span class="xrefLink" id="jumplink-sec3"></span><a href="#sec3" class="sectionLink xref-sec js-xref-sec">3</a>). Even though some efforts have attempted to deal with the lack of labeled data [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>, <span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>, <span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>, <span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>], the challenge has not yet been overcome. A comprehensive list of the methods proposed based on similarity/distance is provided in Table <span class="xrefLink" id="jumplink-TB1"></span><a href="javascript:;" reveal-id="TB1" data-open="TB1" class="link link-reveal link-table xref-fig">1</a>.</p> <a id="225532409" scrollto-destination="225532409"></a> <div content-id="TB1" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB1" data-id="TB1"><span class="label title-label" id="label-42547">Table 1</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532409" aria-describedby="label-42547"> Open in new tab </a></div><div class="caption caption-id-" id="caption-42547"><p class="chapter-para">Similarity/distance-based methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-42547" aria-describedby="
 caption-42547"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SITAR</td><td>Similarity-based Inference of drug-TARgets</td><td>A prediction scheme that integrates multiple drug–drug and gene–gene similarity measures to facilitate the prediction task using logistic regression [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>].</td></tr><tr><td>SRP</td><td>Similarity-Rank-based Predictor</td><td>A lazy supervised non-parametric model using quantitative index to measure the tendency of interacting similar drugs and similar targets to predict DTIs. [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>].</td></tr><tr><td>ECkNN /HLM</td><td>K-Nearest Neighbor Regression with Error Correction or Hubness-aware Local Models</td><td>A kNN method with an error correction method (hubness-aware regression technique) in order to alleviate the detrimental effect of bad hubs [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>] (with substantially different labels from those instances [<span class="xrefLink" id="jumplink-ref100"></span><a href="javascript:;" reveal-id="ref100" data-open="ref100" class="link link-ref link-reveal xref-bibr">100</a>]).</td></tr><tr><td>NP, WP</td><td>Nearest Profile & Weighted Profile</td><td>Given a test drug candidate, it finds a known drug sharing the highest similarity with the test drug, and predict the test drug to interact with target known to interact with the nearest drug [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>].</td></tr><tr><td>MDTI</td><td>MultiviewDTI</td><td>A clustirng algorithm, based on spectral clustring, integrating drug data and target data from both structural and chemical views and the known DTIs [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>].</td></tr><tr><td>STC</td><td>Super-Target Clustering</td><td>A clustering of similar targets by introducing the concept ot super target to handle the missing interactions. [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>].</td></tr><tr><td>LPLNI, LPLNI-II</td><td>Label Propagation method with Linear Neighborhood Information</td><td>A framework in which first drug–drug linear neighborhood similarity is calculated, then the manifold of drugs are taken as similarities and finally unobserved DTIs are predicted using drug–drug similarities, interaction profiles and label propagation [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>].</td></tr><tr><td>WNN-GIP, RLS-WNN</td><td>Weighted Nearest Neighbors-GIP</td><td>A weighted NN algorithm directly incorporated into the GIP method, for constructing an interaction score profile for a new drug compound using information about known compounds [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>].</td></tr><tr><td>BLM</td><td>Bipartite Local Models</td><td>In a bipartite graph model, predicts presence or absence of edges between drug and target using local models trained on known drugs and targets [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>, <span class="xrefLink" id="jumplink-ref108"></span><a href="javascript:;" reveal-id="ref108" data-open="ref108" class="link link-ref link-reveal xref-bibr">108</a>].</td></tr><tr><td>BLM-NII</td><td>BLM with Neighbor-based Interaction-profile Inferring</td><td>An inferring integrated into the BLM method to handle the new candidate problem of pure BLM [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>].</td></tr><tr><td>WBRDTI</td><td>Weighted Bayesian Ranking method</td><td>An improvement of BRDTI method by incorporating inteaction weights for unknown DTs calculated based on known neighboring DTs [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SITAR</td><td>Similarity-based Inference of drug-TARgets</td><td>A prediction scheme that integrates multiple drug–drug and gene–gene similarity measures to facilitate the prediction task using logistic regression [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>].</td></tr><tr><td>SRP</td><td>Similarity-Rank-based Predictor</td><td>A lazy supervised non-parametric model using quantitative index to measure the tendency of interacting similar drugs and similar targets to predict DTIs. [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>].</td></tr><tr><td>ECkNN /HLM</td><td>K-Nearest Neighbor Regression with Error Correction or Hubness-aware Local Models</td><td>A kNN method with an error correction method (hubness-aware regression technique) in order to alleviate the detrimental effect of bad hubs [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>] (with substantially different labels from those instances [<span class="xrefLink" id="jumplink-ref100"></span><a href="javascript:;" reveal-id="ref100" data-open="ref100" class="link link-ref link-reveal xref-bibr">100</a>]).</td></tr><tr><td>NP, WP</td><td>Nearest Profile & Weighted Profile</td><td>Given a test drug candidate, it finds a known drug sharing the highest similarity with the test drug, and predict the test drug to interact with target known to interact with the nearest drug [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>].</td></tr><tr><td>MDTI</td><td>MultiviewDTI</td><td>A clustirng algorithm, based on spectral clustring, integrating drug data and target data from both structural and chemical views and the known DTIs [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>].</td></tr><tr><td>STC</td><td>Super-Target Clustering</td><td>A clustering of similar targets by introducing the concept ot super target to handle the missing interactions. [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>].</td></tr><tr><td>LPLNI, LPLNI-II</td><td>Label Propagation method with Linear Neighborhood Information</td><td>A framework in which first drug–drug linear neighborhood similarity is calculated, then the manifold of drugs are taken as similarities and finally unobserved DTIs are predicted using drug–drug similarities, interaction profiles and label propagation [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>].</td></tr><tr><td>WNN-GIP, RLS-WNN</td><td>Weighted Nearest Neighbors-GIP</td><td>A weighted NN algorithm directly incorporated into the GIP method, for constructing an interaction score profile for a new drug compound using information about known compounds [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>].</td></tr><tr><td>BLM</td><td>Bipartite Local Models</td><td>In a bipartite graph model, predicts presence or absence of edges between drug and target using local models trained on known drugs and targets [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>, <span class="xrefLink" id="jumplink-ref108"></span><a href="javascript:;" reveal-id="ref108" data-open="ref108" class="link link-ref link-reveal xref-bibr">108</a>].</td></tr><tr><td>BLM-NII</td><td>BLM with Neighbor-based Interaction-profile Inferring</td><td>An inferring integrated into the BLM method to handle the new candidate problem of pure BLM [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>].</td></tr><tr><td>WBRDTI</td><td>Weighted Bayesian Ranking method</td><td>An improvement of BRDTI method by incorporating inteaction weights for unknown DTs calculated based on known neighboring DTs [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>].</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB1" data-id="TB1"><span class="label title-label" id="label-42547">Table 1</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532409" aria-describedby="label-42547"> Open in new tab </a></div><div class="caption caption-id-" id="caption-42547"><p class="chapter-para">Similarity/distance-based methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-42547" aria-describedby="
 caption-42547"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SITAR</td><td>Similarity-based Inference of drug-TARgets</td><td>A prediction scheme that integrates multiple drug–drug and gene–gene similarity measures to facilitate the prediction task using logistic regression [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>].</td></tr><tr><td>SRP</td><td>Similarity-Rank-based Predictor</td><td>A lazy supervised non-parametric model using quantitative index to measure the tendency of interacting similar drugs and similar targets to predict DTIs. [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>].</td></tr><tr><td>ECkNN /HLM</td><td>K-Nearest Neighbor Regression with Error Correction or Hubness-aware Local Models</td><td>A kNN method with an error correction method (hubness-aware regression technique) in order to alleviate the detrimental effect of bad hubs [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>] (with substantially different labels from those instances [<span class="xrefLink" id="jumplink-ref100"></span><a href="javascript:;" reveal-id="ref100" data-open="ref100" class="link link-ref link-reveal xref-bibr">100</a>]).</td></tr><tr><td>NP, WP</td><td>Nearest Profile & Weighted Profile</td><td>Given a test drug candidate, it finds a known drug sharing the highest similarity with the test drug, and predict the test drug to interact with target known to interact with the nearest drug [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>].</td></tr><tr><td>MDTI</td><td>MultiviewDTI</td><td>A clustirng algorithm, based on spectral clustring, integrating drug data and target data from both structural and chemical views and the known DTIs [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>].</td></tr><tr><td>STC</td><td>Super-Target Clustering</td><td>A clustering of similar targets by introducing the concept ot super target to handle the missing interactions. [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>].</td></tr><tr><td>LPLNI, LPLNI-II</td><td>Label Propagation method with Linear Neighborhood Information</td><td>A framework in which first drug–drug linear neighborhood similarity is calculated, then the manifold of drugs are taken as similarities and finally unobserved DTIs are predicted using drug–drug similarities, interaction profiles and label propagation [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>].</td></tr><tr><td>WNN-GIP, RLS-WNN</td><td>Weighted Nearest Neighbors-GIP</td><td>A weighted NN algorithm directly incorporated into the GIP method, for constructing an interaction score profile for a new drug compound using information about known compounds [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>].</td></tr><tr><td>BLM</td><td>Bipartite Local Models</td><td>In a bipartite graph model, predicts presence or absence of edges between drug and target using local models trained on known drugs and targets [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>, <span class="xrefLink" id="jumplink-ref108"></span><a href="javascript:;" reveal-id="ref108" data-open="ref108" class="link link-ref link-reveal xref-bibr">108</a>].</td></tr><tr><td>BLM-NII</td><td>BLM with Neighbor-based Interaction-profile Inferring</td><td>An inferring integrated into the BLM method to handle the new candidate problem of pure BLM [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>].</td></tr><tr><td>WBRDTI</td><td>Weighted Bayesian Ranking method</td><td>An improvement of BRDTI method by incorporating inteaction weights for unknown DTs calculated based on known neighboring DTs [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SITAR</td><td>Similarity-based Inference of drug-TARgets</td><td>A prediction scheme that integrates multiple drug–drug and gene–gene similarity measures to facilitate the prediction task using logistic regression [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>].</td></tr><tr><td>SRP</td><td>Similarity-Rank-based Predictor</td><td>A lazy supervised non-parametric model using quantitative index to measure the tendency of interacting similar drugs and similar targets to predict DTIs. [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>].</td></tr><tr><td>ECkNN /HLM</td><td>K-Nearest Neighbor Regression with Error Correction or Hubness-aware Local Models</td><td>A kNN method with an error correction method (hubness-aware regression technique) in order to alleviate the detrimental effect of bad hubs [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>] (with substantially different labels from those instances [<span class="xrefLink" id="jumplink-ref100"></span><a href="javascript:;" reveal-id="ref100" data-open="ref100" class="link link-ref link-reveal xref-bibr">100</a>]).</td></tr><tr><td>NP, WP</td><td>Nearest Profile & Weighted Profile</td><td>Given a test drug candidate, it finds a known drug sharing the highest similarity with the test drug, and predict the test drug to interact with target known to interact with the nearest drug [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>].</td></tr><tr><td>MDTI</td><td>MultiviewDTI</td><td>A clustirng algorithm, based on spectral clustring, integrating drug data and target data from both structural and chemical views and the known DTIs [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>].</td></tr><tr><td>STC</td><td>Super-Target Clustering</td><td>A clustering of similar targets by introducing the concept ot super target to handle the missing interactions. [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>].</td></tr><tr><td>LPLNI, LPLNI-II</td><td>Label Propagation method with Linear Neighborhood Information</td><td>A framework in which first drug–drug linear neighborhood similarity is calculated, then the manifold of drugs are taken as similarities and finally unobserved DTIs are predicted using drug–drug similarities, interaction profiles and label propagation [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>].</td></tr><tr><td>WNN-GIP, RLS-WNN</td><td>Weighted Nearest Neighbors-GIP</td><td>A weighted NN algorithm directly incorporated into the GIP method, for constructing an interaction score profile for a new drug compound using information about known compounds [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>].</td></tr><tr><td>BLM</td><td>Bipartite Local Models</td><td>In a bipartite graph model, predicts presence or absence of edges between drug and target using local models trained on known drugs and targets [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>, <span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>, <span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>, <span class="xrefLink" id="jumplink-ref108"></span><a href="javascript:;" reveal-id="ref108" data-open="ref108" class="link link-ref link-reveal xref-bibr">108</a>].</td></tr><tr><td>BLM-NII</td><td>BLM with Neighbor-based Interaction-profile Inferring</td><td>An inferring integrated into the BLM method to handle the new candidate problem of pure BLM [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>].</td></tr><tr><td>WBRDTI</td><td>Weighted Bayesian Ranking method</td><td>An improvement of BRDTI method by incorporating inteaction weights for unknown DTs calculated based on known neighboring DTs [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>].</td></tr></tbody></table></div></div></div> <h3 scrollto-destination=225532410 id="225532410" class="section-title js-splitscreen-section-title" data-legacy-id=sec2c>2.3 Deep learning methods</h3> <p class="chapter-para">Deep learning is becoming more and more popular given its great performance in many areas, such as speech recognition, image recognition and natural language processing. Applying deep leaning methods to drug discovery has been consistently increasing in recent years [<span class="xrefLink" id="jumplink-ref113"></span><a href="javascript:;" reveal-id="ref113" data-open="ref113" class="link link-ref link-reveal xref-bibr">113</a>, <span class="xrefLink" id="jumplink-ref114"></span><a href="javascript:;" reveal-id="ref114" data-open="ref114" class="link link-ref link-reveal xref-bibr">114</a>].</p><p class="chapter-para">Deep learning approaches appear to overcome certain limitations by reducing the loss of feature information in predicting DTIs. One of the drawbacks in using deep learning methods lays in the fact that there is not always sufficient information available in order to perform deep learning methods. Recently, in order to deal with high dimensional and oftentimes noisy data in DTI predictions in general and in drug repurposing in particular, authors in [<span class="xrefLink" id="jumplink-ref115 ref116 ref117"></span><a href="javascript:;" reveal-id="ref115 ref116 ref117" data-open="ref115 ref116 ref117" class="link link-ref link-reveal xref-bibr">115–117</a>] proposed and developed deep learning algorithms in the DTI’s machine learning approaches.</p><p class="chapter-para">Most of the deep learning-based DTI prediction methods consist of two major steps: generating feature vectors and then applying deep learning to known DTIs. Usually, three types of properties (i.e. biological, topological and physico-chemical information) of drugs and/or targets can be used for generating feature vectors/matrix for deep learning based DTI methods. In recently published works [<span class="xrefLink" id="jumplink-ref116 ref117 ref118 ref119 ref120 ref121 ref122"></span><a href="javascript:;" reveal-id="ref116 ref117 ref118 ref119 ref120 ref121 ref122" data-open="ref116 ref117 ref118 ref119 ref120 ref121 ref122" class="link link-ref link-reveal xref-bibr">116–122</a>], methods such as deep belief neural networks [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>, <span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>], convolutional neural networks [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>, <span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>] and multiple layer perceptrons [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>, <span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>] were used to establish DTI prediction programs.</p><p class="chapter-para">In [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>], instead of using a bipartite network to represent the DTI, a Tripartite Linked Network [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>], derived from the existing linked open datasets in the biomedical domain [<span class="xrefLink" id="jumplink-ref125"></span><a href="javascript:;" reveal-id="ref125" data-open="ref125" class="link link-ref link-reveal xref-bibr">125</a>] were used for new DTI predictions. One advantage of methods employing deep learning over the state-of-the-art feature extraction methods and SVM classifiers is the ability to mine the hidden interactions between drugs and targets.</p><p class="chapter-para">Although all of the aforementioned deep learning methods show good performance, there is room for improvement in several aspects. First, creating robust negative datasets for supervised deep learning method is a challenging task. Most previously published deep learning based DTI prediction programs are supervised machine learning methods, so how to establish an unbiased negative DTI dataset for model fitting and testing is a key step. In addition, DTI prediction is to discover new DTIs. How to select real no-interaction drug–target pairs is a tricky task. Second, with more and more different types of drug/target data available, how to incorporate heterogonous data into high-dimensional features from drug and/or target for deep learning methods is also a challenge. Last but not least, deep learning methods that show great performance on the testing dataset do not mean they also can achieve great performance in real drug discovery. More details about applying deep learning in drug discovery can be found in [<span class="xrefLink" id="jumplink-ref126"></span><a href="javascript:;" reveal-id="ref126" data-open="ref126" class="link link-ref link-reveal xref-bibr">126</a>]. In Table <span class="xrefLink" id="jumplink-TB2"></span><a href="javascript:;" reveal-id="TB2" data-open="TB2" class="link link-reveal link-table xref-fig">2</a>, a brief list of deep learning-based methods mentioned in this paper is provided.</p> <a id="225532416" scrollto-destination="225532416"></a> <div content-id="TB2" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB2" data-id="TB2"><span class="label title-label" id="label-26185">Table 2</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532416" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Deep learning methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DeepDTIs</td><td>Deep Learning in predicting DTIs</td><td>A deep-learning approach utilizing DBN [<span class="xrefLink" id="jumplink-ref123"></span><a href="javascript:;" reveal-id="ref123" data-open="ref123" class="link link-ref link-reveal xref-bibr">123</a>] to abstract raw input vectors and predict new DTIs between FDA approved drugs and targets [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>].</td></tr><tr><td>DeepWalk</td><td></td><td>A deep learning similarity-based DTI prediction method based on the topology of multipartite network of the existing drugs and targets [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>].</td></tr><tr><td>AutoDNP</td><td>Stacked Autoencoder Deep Neural Network</td><td>A deep learning computational method with an ensemble classifier using stacked Autoencoder.[<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>].</td></tr><tr><td>DeepConv-DTI</td><td>Deep learning with convolution-DTI</td><td>A deep learning method capturing local residue patterns of proteins participating in DTIs[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>].</td></tr><tr><td>LASSO-DNN</td><td>Least absolute shrinkage and selection operator-Deep Neural Network</td><td>A deep learning method based on features extracted from the LASSO regression models fitted using the protein-specific and drug-specific features respectively [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>].</td></tr><tr><td>DeepDTA</td><td>Deep DT Binding Affinity Prediction</td><td>A deep learning-based model using only character representations (raw sequence information) for both drugs and targets simply [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>].</td></tr><tr><td>DeepNP</td><td>Deep Neural Representation</td><td>An interpretable end-to-end deep learning architecture to predict DTIs from low level representations [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>].</td></tr><tr><td>DeepTrans</td><td>Deep Transcriptome data</td><td>A framework for DTI prediction based on transcriptome data in the L1000 database gathered from drug perturbation and gene knockout trials [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DeepDTIs</td><td>Deep Learning in predicting DTIs</td><td>A deep-learning approach utilizing DBN [<span class="xrefLink" id="jumplink-ref123"></span><a href="javascript:;" reveal-id="ref123" data-open="ref123" class="link link-ref link-reveal xref-bibr">123</a>] to abstract raw input vectors and predict new DTIs between FDA approved drugs and targets [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>].</td></tr><tr><td>DeepWalk</td><td></td><td>A deep learning similarity-based DTI prediction method based on the topology of multipartite network of the existing drugs and targets [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>].</td></tr><tr><td>AutoDNP</td><td>Stacked Autoencoder Deep Neural Network</td><td>A deep learning computational method with an ensemble classifier using stacked Autoencoder.[<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>].</td></tr><tr><td>DeepConv-DTI</td><td>Deep learning with convolution-DTI</td><td>A deep learning method capturing local residue patterns of proteins participating in DTIs[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>].</td></tr><tr><td>LASSO-DNN</td><td>Least absolute shrinkage and selection operator-Deep Neural Network</td><td>A deep learning method based on features extracted from the LASSO regression models fitted using the protein-specific and drug-specific features respectively [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>].</td></tr><tr><td>DeepDTA</td><td>Deep DT Binding Affinity Prediction</td><td>A deep learning-based model using only character representations (raw sequence information) for both drugs and targets simply [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>].</td></tr><tr><td>DeepNP</td><td>Deep Neural Representation</td><td>An interpretable end-to-end deep learning architecture to predict DTIs from low level representations [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>].</td></tr><tr><td>DeepTrans</td><td>Deep Transcriptome data</td><td>A framework for DTI prediction based on transcriptome data in the L1000 database gathered from drug perturbation and gene knockout trials [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>].</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB2" data-id="TB2"><span class="label title-label" id="label-26185">Table 2</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532416" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Deep learning methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DeepDTIs</td><td>Deep Learning in predicting DTIs</td><td>A deep-learning approach utilizing DBN [<span class="xrefLink" id="jumplink-ref123"></span><a href="javascript:;" reveal-id="ref123" data-open="ref123" class="link link-ref link-reveal xref-bibr">123</a>] to abstract raw input vectors and predict new DTIs between FDA approved drugs and targets [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>].</td></tr><tr><td>DeepWalk</td><td></td><td>A deep learning similarity-based DTI prediction method based on the topology of multipartite network of the existing drugs and targets [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>].</td></tr><tr><td>AutoDNP</td><td>Stacked Autoencoder Deep Neural Network</td><td>A deep learning computational method with an ensemble classifier using stacked Autoencoder.[<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>].</td></tr><tr><td>DeepConv-DTI</td><td>Deep learning with convolution-DTI</td><td>A deep learning method capturing local residue patterns of proteins participating in DTIs[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>].</td></tr><tr><td>LASSO-DNN</td><td>Least absolute shrinkage and selection operator-Deep Neural Network</td><td>A deep learning method based on features extracted from the LASSO regression models fitted using the protein-specific and drug-specific features respectively [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>].</td></tr><tr><td>DeepDTA</td><td>Deep DT Binding Affinity Prediction</td><td>A deep learning-based model using only character representations (raw sequence information) for both drugs and targets simply [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>].</td></tr><tr><td>DeepNP</td><td>Deep Neural Representation</td><td>An interpretable end-to-end deep learning architecture to predict DTIs from low level representations [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>].</td></tr><tr><td>DeepTrans</td><td>Deep Transcriptome data</td><td>A framework for DTI prediction based on transcriptome data in the L1000 database gathered from drug perturbation and gene knockout trials [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DeepDTIs</td><td>Deep Learning in predicting DTIs</td><td>A deep-learning approach utilizing DBN [<span class="xrefLink" id="jumplink-ref123"></span><a href="javascript:;" reveal-id="ref123" data-open="ref123" class="link link-ref link-reveal xref-bibr">123</a>] to abstract raw input vectors and predict new DTIs between FDA approved drugs and targets [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>].</td></tr><tr><td>DeepWalk</td><td></td><td>A deep learning similarity-based DTI prediction method based on the topology of multipartite network of the existing drugs and targets [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>].</td></tr><tr><td>AutoDNP</td><td>Stacked Autoencoder Deep Neural Network</td><td>A deep learning computational method with an ensemble classifier using stacked Autoencoder.[<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>].</td></tr><tr><td>DeepConv-DTI</td><td>Deep learning with convolution-DTI</td><td>A deep learning method capturing local residue patterns of proteins participating in DTIs[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>].</td></tr><tr><td>LASSO-DNN</td><td>Least absolute shrinkage and selection operator-Deep Neural Network</td><td>A deep learning method based on features extracted from the LASSO regression models fitted using the protein-specific and drug-specific features respectively [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>].</td></tr><tr><td>DeepDTA</td><td>Deep DT Binding Affinity Prediction</td><td>A deep learning-based model using only character representations (raw sequence information) for both drugs and targets simply [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>].</td></tr><tr><td>DeepNP</td><td>Deep Neural Representation</td><td>An interpretable end-to-end deep learning architecture to predict DTIs from low level representations [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>].</td></tr><tr><td>DeepTrans</td><td>Deep Transcriptome data</td><td>A framework for DTI prediction based on transcriptome data in the L1000 database gathered from drug perturbation and gene knockout trials [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>].</td></tr></tbody></table></div></div></div> <a id="225532417" scrollto-destination="225532417"></a> <div content-id="TB3" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB3" data-id="TB3"><span class="label title-label" id="label-26185">Table 3</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532417" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Feature-based methods: part I</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SVM, KSVM, MH-SVM</td><td>Support Vector Machine</td><td>A support vector machine constructs a hyperplane or set of hyperplanes, which can be used for prediction of presence or absence of interaction between drugs and targets [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>, <span class="xrefLink" id="jumplink-ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141"></span><a href="javascript:;" reveal-id="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" data-open="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" class="link link-ref link-reveal xref-bibr">127–141</a>].</td></tr><tr><td>BGL/KRM</td><td>Bipartite Graph Learning or Kernel Regression-based Method</td><td>In a bipartite graph model, predicts the presence or absence of edges between drug and target based on graph-based similarity to known drugs and targets in a unified Euclidean space of chemical and genomic space called pharmacological space [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref142"></span><a href="javascript:;" reveal-id="ref142" data-open="ref142" class="link link-ref link-reveal xref-bibr">142</a>, <span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>].</td></tr><tr><td>NetLapRLS</td><td>RLS with kernels derived from known DTIs</td><td>The improved version of LapRLS by incorporating a new kernel established from the known DTI network [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>].</td></tr><tr><td>PKR</td><td>Pairwise Kernel Regression</td><td>A regression model similar to KRM without requirement of any unified chemical and genomic space [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>].</td></tr><tr><td>RF, DDR</td><td>Random Forest</td><td>A robust model against the overfitting problem of traditional statistical methods that performs more efficiently for large-scale databases [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>, <span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>, <span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>] (using [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref146 ref147 ref148 ref149 ref150"></span><a href="javascript:;" reveal-id="ref146 ref147 ref148 ref149 ref150" data-open="ref146 ref147 ref148 ref149 ref150" class="link link-ref link-reveal xref-bibr">146–150</a>]).</td></tr><tr><td>iDTI-ESBoost</td><td></td><td>A prediction model for identification of DTIs using evolutionary and structural features [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>].</td></tr><tr><td>PUDT</td><td>Positive-Unlabeled learning for DT prediction</td><td>A framework treating unknown DTI as unlabeled samples and using weighted SVM predictor [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>].</td></tr><tr><td>GIP</td><td>RLS with Gaussian interaction profile kernel</td><td>An RLS algorithm that incorporates the topology of known DTI network as source information through GIP kernel [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>].</td></tr><tr><td>RLS</td><td>Regularized Least Square, also RLS-Kron, RLS-avg, LapRLS, KRLS, RLS-KF, KronRLS-MKL</td><td>A semi-supervised framework that incorporates known DTIs and unknown DTIs in a general-purpose learner.[<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>, <span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>, <span class="xrefLink" id="jumplink-ref153 ref154 ref155 ref156 ref157 ref158"></span><a href="javascript:;" reveal-id="ref153 ref154 ref155 ref156 ref157 ref158" data-open="ref153 ref154 ref155 ref156 ref157 ref158" class="link link-ref link-reveal xref-bibr">153–158</a>].</td></tr><tr><td></td><td>SimBoost, SimBoostQuant</td><td>A non-linear method for continuous DT binding affinity prediction and an extended version SimBoostQuant, using quantile regression to estimate a prediction interval as a measure of confidence. [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SVM, KSVM, MH-SVM</td><td>Support Vector Machine</td><td>A support vector machine constructs a hyperplane or set of hyperplanes, which can be used for prediction of presence or absence of interaction between drugs and targets [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>, <span class="xrefLink" id="jumplink-ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141"></span><a href="javascript:;" reveal-id="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" data-open="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" class="link link-ref link-reveal xref-bibr">127–141</a>].</td></tr><tr><td>BGL/KRM</td><td>Bipartite Graph Learning or Kernel Regression-based Method</td><td>In a bipartite graph model, predicts the presence or absence of edges between drug and target based on graph-based similarity to known drugs and targets in a unified Euclidean space of chemical and genomic space called pharmacological space [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref142"></span><a href="javascript:;" reveal-id="ref142" data-open="ref142" class="link link-ref link-reveal xref-bibr">142</a>, <span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>].</td></tr><tr><td>NetLapRLS</td><td>RLS with kernels derived from known DTIs</td><td>The improved version of LapRLS by incorporating a new kernel established from the known DTI network [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>].</td></tr><tr><td>PKR</td><td>Pairwise Kernel Regression</td><td>A regression model similar to KRM without requirement of any unified chemical and genomic space [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>].</td></tr><tr><td>RF, DDR</td><td>Random Forest</td><td>A robust model against the overfitting problem of traditional statistical methods that performs more efficiently for large-scale databases [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>, <span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>, <span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>] (using [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref146 ref147 ref148 ref149 ref150"></span><a href="javascript:;" reveal-id="ref146 ref147 ref148 ref149 ref150" data-open="ref146 ref147 ref148 ref149 ref150" class="link link-ref link-reveal xref-bibr">146–150</a>]).</td></tr><tr><td>iDTI-ESBoost</td><td></td><td>A prediction model for identification of DTIs using evolutionary and structural features [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>].</td></tr><tr><td>PUDT</td><td>Positive-Unlabeled learning for DT prediction</td><td>A framework treating unknown DTI as unlabeled samples and using weighted SVM predictor [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>].</td></tr><tr><td>GIP</td><td>RLS with Gaussian interaction profile kernel</td><td>An RLS algorithm that incorporates the topology of known DTI network as source information through GIP kernel [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>].</td></tr><tr><td>RLS</td><td>Regularized Least Square, also RLS-Kron, RLS-avg, LapRLS, KRLS, RLS-KF, KronRLS-MKL</td><td>A semi-supervised framework that incorporates known DTIs and unknown DTIs in a general-purpose learner.[<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>, <span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>, <span class="xrefLink" id="jumplink-ref153 ref154 ref155 ref156 ref157 ref158"></span><a href="javascript:;" reveal-id="ref153 ref154 ref155 ref156 ref157 ref158" data-open="ref153 ref154 ref155 ref156 ref157 ref158" class="link link-ref link-reveal xref-bibr">153–158</a>].</td></tr><tr><td></td><td>SimBoost, SimBoostQuant</td><td>A non-linear method for continuous DT binding affinity prediction and an extended version SimBoostQuant, using quantile regression to estimate a prediction interval as a measure of confidence. [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>].</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB3" data-id="TB3"><span class="label title-label" id="label-26185">Table 3</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532417" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Feature-based methods: part I</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SVM, KSVM, MH-SVM</td><td>Support Vector Machine</td><td>A support vector machine constructs a hyperplane or set of hyperplanes, which can be used for prediction of presence or absence of interaction between drugs and targets [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>, <span class="xrefLink" id="jumplink-ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141"></span><a href="javascript:;" reveal-id="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" data-open="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" class="link link-ref link-reveal xref-bibr">127–141</a>].</td></tr><tr><td>BGL/KRM</td><td>Bipartite Graph Learning or Kernel Regression-based Method</td><td>In a bipartite graph model, predicts the presence or absence of edges between drug and target based on graph-based similarity to known drugs and targets in a unified Euclidean space of chemical and genomic space called pharmacological space [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref142"></span><a href="javascript:;" reveal-id="ref142" data-open="ref142" class="link link-ref link-reveal xref-bibr">142</a>, <span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>].</td></tr><tr><td>NetLapRLS</td><td>RLS with kernels derived from known DTIs</td><td>The improved version of LapRLS by incorporating a new kernel established from the known DTI network [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>].</td></tr><tr><td>PKR</td><td>Pairwise Kernel Regression</td><td>A regression model similar to KRM without requirement of any unified chemical and genomic space [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>].</td></tr><tr><td>RF, DDR</td><td>Random Forest</td><td>A robust model against the overfitting problem of traditional statistical methods that performs more efficiently for large-scale databases [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>, <span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>, <span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>] (using [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref146 ref147 ref148 ref149 ref150"></span><a href="javascript:;" reveal-id="ref146 ref147 ref148 ref149 ref150" data-open="ref146 ref147 ref148 ref149 ref150" class="link link-ref link-reveal xref-bibr">146–150</a>]).</td></tr><tr><td>iDTI-ESBoost</td><td></td><td>A prediction model for identification of DTIs using evolutionary and structural features [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>].</td></tr><tr><td>PUDT</td><td>Positive-Unlabeled learning for DT prediction</td><td>A framework treating unknown DTI as unlabeled samples and using weighted SVM predictor [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>].</td></tr><tr><td>GIP</td><td>RLS with Gaussian interaction profile kernel</td><td>An RLS algorithm that incorporates the topology of known DTI network as source information through GIP kernel [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>].</td></tr><tr><td>RLS</td><td>Regularized Least Square, also RLS-Kron, RLS-avg, LapRLS, KRLS, RLS-KF, KronRLS-MKL</td><td>A semi-supervised framework that incorporates known DTIs and unknown DTIs in a general-purpose learner.[<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>, <span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>, <span class="xrefLink" id="jumplink-ref153 ref154 ref155 ref156 ref157 ref158"></span><a href="javascript:;" reveal-id="ref153 ref154 ref155 ref156 ref157 ref158" data-open="ref153 ref154 ref155 ref156 ref157 ref158" class="link link-ref link-reveal xref-bibr">153–158</a>].</td></tr><tr><td></td><td>SimBoost, SimBoostQuant</td><td>A non-linear method for continuous DT binding affinity prediction and an extended version SimBoostQuant, using quantile regression to estimate a prediction interval as a measure of confidence. [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>SVM, KSVM, MH-SVM</td><td>Support Vector Machine</td><td>A support vector machine constructs a hyperplane or set of hyperplanes, which can be used for prediction of presence or absence of interaction between drugs and targets [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>, <span class="xrefLink" id="jumplink-ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141"></span><a href="javascript:;" reveal-id="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" data-open="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141" class="link link-ref link-reveal xref-bibr">127–141</a>].</td></tr><tr><td>BGL/KRM</td><td>Bipartite Graph Learning or Kernel Regression-based Method</td><td>In a bipartite graph model, predicts the presence or absence of edges between drug and target based on graph-based similarity to known drugs and targets in a unified Euclidean space of chemical and genomic space called pharmacological space [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref142"></span><a href="javascript:;" reveal-id="ref142" data-open="ref142" class="link link-ref link-reveal xref-bibr">142</a>, <span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>].</td></tr><tr><td>NetLapRLS</td><td>RLS with kernels derived from known DTIs</td><td>The improved version of LapRLS by incorporating a new kernel established from the known DTI network [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>].</td></tr><tr><td>PKR</td><td>Pairwise Kernel Regression</td><td>A regression model similar to KRM without requirement of any unified chemical and genomic space [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>].</td></tr><tr><td>RF, DDR</td><td>Random Forest</td><td>A robust model against the overfitting problem of traditional statistical methods that performs more efficiently for large-scale databases [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>, <span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>, <span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>] (using [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref146 ref147 ref148 ref149 ref150"></span><a href="javascript:;" reveal-id="ref146 ref147 ref148 ref149 ref150" data-open="ref146 ref147 ref148 ref149 ref150" class="link link-ref link-reveal xref-bibr">146–150</a>]).</td></tr><tr><td>iDTI-ESBoost</td><td></td><td>A prediction model for identification of DTIs using evolutionary and structural features [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>].</td></tr><tr><td>PUDT</td><td>Positive-Unlabeled learning for DT prediction</td><td>A framework treating unknown DTI as unlabeled samples and using weighted SVM predictor [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>].</td></tr><tr><td>GIP</td><td>RLS with Gaussian interaction profile kernel</td><td>An RLS algorithm that incorporates the topology of known DTI network as source information through GIP kernel [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>].</td></tr><tr><td>RLS</td><td>Regularized Least Square, also RLS-Kron, RLS-avg, LapRLS, KRLS, RLS-KF, KronRLS-MKL</td><td>A semi-supervised framework that incorporates known DTIs and unknown DTIs in a general-purpose learner.[<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>, <span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>, <span class="xrefLink" id="jumplink-ref153 ref154 ref155 ref156 ref157 ref158"></span><a href="javascript:;" reveal-id="ref153 ref154 ref155 ref156 ref157 ref158" data-open="ref153 ref154 ref155 ref156 ref157 ref158" class="link link-ref link-reveal xref-bibr">153–158</a>].</td></tr><tr><td></td><td>SimBoost, SimBoostQuant</td><td>A non-linear method for continuous DT binding affinity prediction and an extended version SimBoostQuant, using quantile regression to estimate a prediction interval as a measure of confidence. [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>].</td></tr></tbody></table></div></div></div> <h3 scrollto-destination=225532418 id="225532418" class="section-title js-splitscreen-section-title" data-legacy-id=sec2d>2.4 Feature-based methods</h3> <div class="
 block-child-p
 ">The vast majority of machine learning methods performing DTI prediction fall into this category. It is a broad range of methods including SVM, tree-based methods and other kernel-based methods. Any pairs of drugs and targets would be represented in terms of feature vectors with certain length, often with binary labels that classify the pair vectors into two classes with positive and negative interaction. In other words, assuming feature space <span class="inline-formula no-formula-id">|$\mathbf F$|</span> where <div class="formula-wrap"><div class="disp-formula" id="jumplink-" content-id=""><div class="tex-math display-math">$$\begin{equation*} \mathbf F= \bigg\{f:=d\oplus t\, \Big|\, d=[d_1, d_2, \cdots, d_n] \ \&\,\, t=[t_1, t_2, \cdots, t_m] \bigg\},\notag \end{equation*}$$</div></div></div> where <span class="inline-formula no-formula-id">|$d$|</span> and <span class="inline-formula no-formula-id">|$t$|</span> denote the target and drug feature vectors of length <span class="inline-formula no-formula-id">|$n$|</span> and <span class="inline-formula no-formula-id">|$m$|⁠</span>, respectively.</div><p class="chapter-para">Once the feature space is defined, assorted machine learning methods can be established to perform the DTI prediction task [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>, <span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>, <span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>, <span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>, <span class="xrefLink" id="jumplink-ref89"></span><a href="javascript:;" reveal-id="ref89" data-open="ref89" class="link link-ref link-reveal xref-bibr">89</a>, <span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>, <span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>, <span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>, <span class="xrefLink" id="jumplink-ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141 ref142 ref143 ref144 ref145 ref146 ref147 ref148 ref149 ref150 ref151 ref152 ref153 ref154 ref155 ref156 ref157 ref158 ref159 ref160 ref161 ref162 ref163 ref164 ref165 ref166 ref167 ref168 ref169 ref170 ref171 ref172 ref173 ref174 ref175 ref176 ref177 ref178"></span><a href="javascript:;" reveal-id="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141 ref142 ref143 ref144 ref145 ref146 ref147 ref148 ref149 ref150 ref151 ref152 ref153 ref154 ref155 ref156 ref157 ref158 ref159 ref160 ref161 ref162 ref163 ref164 ref165 ref166 ref167 ref168 ref169 ref170 ref171 ref172 ref173 ref174 ref175 ref176 ref177 ref178" data-open="ref127 ref128 ref129 ref130 ref131 ref132 ref133 ref134 ref135 ref136 ref137 ref138 ref139 ref140 ref141 ref142 ref143 ref144 ref145 ref146 ref147 ref148 ref149 ref150 ref151 ref152 ref153 ref154 ref155 ref156 ref157 ref158 ref159 ref160 ref161 ref162 ref163 ref164 ref165 ref166 ref167 ref168 ref169 ref170 ref171 ref172 ref173 ref174 ref175 ref176 ref177 ref178" class="link link-ref link-reveal xref-bibr">127–178</a>]. The lack of 3D structures of membrane proteins prevents extracting the main features, which otherwise would have yielded to better prediction performances. Tables <span class="xrefLink" id="jumplink-TB3"></span><a href="javascript:;" reveal-id="TB3" data-open="TB3" class="link link-reveal link-table xref-fig">3</a> and <span class="xrefLink" id="jumplink-TB4"></span><a href="javascript:;" reveal-id="TB4" data-open="TB4" class="link link-reveal link-table xref-fig">4</a> provides a broad list of feature-based methods along with a short description and the papers in which those methods were proposed and employed.</p> <a id="225532421" scrollto-destination="225532421"></a> <div content-id="TB4" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB4" data-id="TB4"><span class="label title-label" id="label-26185">Table 4</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532421" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Feature-based methods: part II</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>RFDT</td><td>Rotation Forest-based DTI prediction</td><td>A computational model based on the assumptions that the protein sequences are encoded as Position Specific Scoring Matrix (PSSM) [<span class="xrefLink" id="jumplink-ref160"></span><a href="javascript:;" reveal-id="ref160" data-open="ref160" class="link link-ref link-reveal xref-bibr">160</a>] descriptor and the drug molecules are encoded as fingerprint feature vector [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>].</td></tr><tr><td>DrugRPE</td><td></td><td>A random projection ensemble approach for based on the REPTree algorithm [<span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>] and using random projection [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>, <span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>, <span class="xrefLink" id="jumplink-ref163"></span><a href="javascript:;" reveal-id="ref163" data-open="ref163" class="link link-ref link-reveal xref-bibr">163</a>, <span class="xrefLink" id="jumplink-ref163 ref164 ref165"></span><a href="javascript:;" reveal-id="ref163 ref164 ref165" data-open="ref163 ref164 ref165" class="link link-ref link-reveal xref-bibr">163–165</a>].</td></tr><tr><td>CGBVS</td><td>ChemoGenomics-Based Virtual Screening</td><td>A kernel-based state-of-the-art method using virtual screening (VS) [<span class="xrefLink" id="jumplink-ref89"></span><a href="javascript:;" reveal-id="ref89" data-open="ref89" class="link link-ref link-reveal xref-bibr">89</a>] and pairwise kernel method (PKM) [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>] [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>].</td></tr><tr><td></td><td>DASPfind</td><td>A computational DTI prediction method relying on the topological structure of the heterogeneous graph interaction model [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>].</td></tr><tr><td>SAR</td><td>Structure-Activity Relationship method</td><td>A screening of chemical compounds method for classification problem of DTIs using protein sequences and drug topological structures [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref168"></span><a href="javascript:;" reveal-id="ref168" data-open="ref168" class="link link-ref link-reveal xref-bibr">168</a>].</td></tr><tr><td>DVM</td><td>Discriminative Vector Machine</td><td>A classifier and a method by formulating the DTIs as an extended SAR classification problem [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>] (using principal component analysis (PCA) method [<span class="xrefLink" id="jumplink-ref170"></span><a href="javascript:;" reveal-id="ref170" data-open="ref170" class="link link-ref link-reveal xref-bibr">170</a>]).</td></tr><tr><td>EnsL</td><td>Ensemble Learning (with dimensionality reduction, or class imbalance-aware)</td><td>A framework predicts DTI based on average voting of its base classifiers: Decision Tree (EnsemDT) [<span class="xrefLink" id="jumplink-ref171 ref172 ref173"></span><a href="javascript:;" reveal-id="ref171 ref172 ref173" data-open="ref171 ref172 ref173" class="link link-ref link-reveal xref-bibr">171–173</a>] (based on Singular Value Decomposition (SVD), Partial Least Squares (PLS) [<span class="xrefLink" id="jumplink-ref174"></span><a href="javascript:;" reveal-id="ref174" data-open="ref174" class="link link-ref link-reveal xref-bibr">174</a>] and Laplacian Eigenmaps (LapEig) [<span class="xrefLink" id="jumplink-ref175"></span><a href="javascript:;" reveal-id="ref175" data-open="ref175" class="link link-ref link-reveal xref-bibr">175</a>]),Kernel Ridge Regression (EnsemKRR), Random Forest (EnsemRF) [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>], stacked (EnsemSTACK) [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>], DrugE-Rank [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>].</td></tr><tr><td>BE-DTI</td><td>Bagging-based Ensemble method</td><td>A bagging-based ensemble framework that involves dimensionality reduction and active learning [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>RFDT</td><td>Rotation Forest-based DTI prediction</td><td>A computational model based on the assumptions that the protein sequences are encoded as Position Specific Scoring Matrix (PSSM) [<span class="xrefLink" id="jumplink-ref160"></span><a href="javascript:;" reveal-id="ref160" data-open="ref160" class="link link-ref link-reveal xref-bibr">160</a>] descriptor and the drug molecules are encoded as fingerprint feature vector [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>].</td></tr><tr><td>DrugRPE</td><td></td><td>A random projection ensemble approach for based on the REPTree algorithm [<span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>] and using random projection [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>, <span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>, <span class="xrefLink" id="jumplink-ref163"></span><a href="javascript:;" reveal-id="ref163" data-open="ref163" class="link link-ref link-reveal xref-bibr">163</a>, <span class="xrefLink" id="jumplink-ref163 ref164 ref165"></span><a href="javascript:;" reveal-id="ref163 ref164 ref165" data-open="ref163 ref164 ref165" class="link link-ref link-reveal xref-bibr">163–165</a>].</td></tr><tr><td>CGBVS</td><td>ChemoGenomics-Based Virtual Screening</td><td>A kernel-based state-of-the-art method using virtual screening (VS) [<span class="xrefLink" id="jumplink-ref89"></span><a href="javascript:;" reveal-id="ref89" data-open="ref89" class="link link-ref link-reveal xref-bibr">89</a>] and pairwise kernel method (PKM) [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>] [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>].</td></tr><tr><td></td><td>DASPfind</td><td>A computational DTI prediction method relying on the topological structure of the heterogeneous graph interaction model [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>].</td></tr><tr><td>SAR</td><td>Structure-Activity Relationship method</td><td>A screening of chemical compounds method for classification problem of DTIs using protein sequences and drug topological structures [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref168"></span><a href="javascript:;" reveal-id="ref168" data-open="ref168" class="link link-ref link-reveal xref-bibr">168</a>].</td></tr><tr><td>DVM</td><td>Discriminative Vector Machine</td><td>A classifier and a method by formulating the DTIs as an extended SAR classification problem [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>] (using principal component analysis (PCA) method [<span class="xrefLink" id="jumplink-ref170"></span><a href="javascript:;" reveal-id="ref170" data-open="ref170" class="link link-ref link-reveal xref-bibr">170</a>]).</td></tr><tr><td>EnsL</td><td>Ensemble Learning (with dimensionality reduction, or class imbalance-aware)</td><td>A framework predicts DTI based on average voting of its base classifiers: Decision Tree (EnsemDT) [<span class="xrefLink" id="jumplink-ref171 ref172 ref173"></span><a href="javascript:;" reveal-id="ref171 ref172 ref173" data-open="ref171 ref172 ref173" class="link link-ref link-reveal xref-bibr">171–173</a>] (based on Singular Value Decomposition (SVD), Partial Least Squares (PLS) [<span class="xrefLink" id="jumplink-ref174"></span><a href="javascript:;" reveal-id="ref174" data-open="ref174" class="link link-ref link-reveal xref-bibr">174</a>] and Laplacian Eigenmaps (LapEig) [<span class="xrefLink" id="jumplink-ref175"></span><a href="javascript:;" reveal-id="ref175" data-open="ref175" class="link link-ref link-reveal xref-bibr">175</a>]),Kernel Ridge Regression (EnsemKRR), Random Forest (EnsemRF) [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>], stacked (EnsemSTACK) [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>], DrugE-Rank [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>].</td></tr><tr><td>BE-DTI</td><td>Bagging-based Ensemble method</td><td>A bagging-based ensemble framework that involves dimensionality reduction and active learning [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>].</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB4" data-id="TB4"><span class="label title-label" id="label-26185">Table 4</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532421" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Feature-based methods: part II</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>RFDT</td><td>Rotation Forest-based DTI prediction</td><td>A computational model based on the assumptions that the protein sequences are encoded as Position Specific Scoring Matrix (PSSM) [<span class="xrefLink" id="jumplink-ref160"></span><a href="javascript:;" reveal-id="ref160" data-open="ref160" class="link link-ref link-reveal xref-bibr">160</a>] descriptor and the drug molecules are encoded as fingerprint feature vector [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>].</td></tr><tr><td>DrugRPE</td><td></td><td>A random projection ensemble approach for based on the REPTree algorithm [<span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>] and using random projection [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>, <span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>, <span class="xrefLink" id="jumplink-ref163"></span><a href="javascript:;" reveal-id="ref163" data-open="ref163" class="link link-ref link-reveal xref-bibr">163</a>, <span class="xrefLink" id="jumplink-ref163 ref164 ref165"></span><a href="javascript:;" reveal-id="ref163 ref164 ref165" data-open="ref163 ref164 ref165" class="link link-ref link-reveal xref-bibr">163–165</a>].</td></tr><tr><td>CGBVS</td><td>ChemoGenomics-Based Virtual Screening</td><td>A kernel-based state-of-the-art method using virtual screening (VS) [<span class="xrefLink" id="jumplink-ref89"></span><a href="javascript:;" reveal-id="ref89" data-open="ref89" class="link link-ref link-reveal xref-bibr">89</a>] and pairwise kernel method (PKM) [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>] [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>].</td></tr><tr><td></td><td>DASPfind</td><td>A computational DTI prediction method relying on the topological structure of the heterogeneous graph interaction model [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>].</td></tr><tr><td>SAR</td><td>Structure-Activity Relationship method</td><td>A screening of chemical compounds method for classification problem of DTIs using protein sequences and drug topological structures [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref168"></span><a href="javascript:;" reveal-id="ref168" data-open="ref168" class="link link-ref link-reveal xref-bibr">168</a>].</td></tr><tr><td>DVM</td><td>Discriminative Vector Machine</td><td>A classifier and a method by formulating the DTIs as an extended SAR classification problem [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>] (using principal component analysis (PCA) method [<span class="xrefLink" id="jumplink-ref170"></span><a href="javascript:;" reveal-id="ref170" data-open="ref170" class="link link-ref link-reveal xref-bibr">170</a>]).</td></tr><tr><td>EnsL</td><td>Ensemble Learning (with dimensionality reduction, or class imbalance-aware)</td><td>A framework predicts DTI based on average voting of its base classifiers: Decision Tree (EnsemDT) [<span class="xrefLink" id="jumplink-ref171 ref172 ref173"></span><a href="javascript:;" reveal-id="ref171 ref172 ref173" data-open="ref171 ref172 ref173" class="link link-ref link-reveal xref-bibr">171–173</a>] (based on Singular Value Decomposition (SVD), Partial Least Squares (PLS) [<span class="xrefLink" id="jumplink-ref174"></span><a href="javascript:;" reveal-id="ref174" data-open="ref174" class="link link-ref link-reveal xref-bibr">174</a>] and Laplacian Eigenmaps (LapEig) [<span class="xrefLink" id="jumplink-ref175"></span><a href="javascript:;" reveal-id="ref175" data-open="ref175" class="link link-ref link-reveal xref-bibr">175</a>]),Kernel Ridge Regression (EnsemKRR), Random Forest (EnsemRF) [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>], stacked (EnsemSTACK) [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>], DrugE-Rank [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>].</td></tr><tr><td>BE-DTI</td><td>Bagging-based Ensemble method</td><td>A bagging-based ensemble framework that involves dimensionality reduction and active learning [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>RFDT</td><td>Rotation Forest-based DTI prediction</td><td>A computational model based on the assumptions that the protein sequences are encoded as Position Specific Scoring Matrix (PSSM) [<span class="xrefLink" id="jumplink-ref160"></span><a href="javascript:;" reveal-id="ref160" data-open="ref160" class="link link-ref link-reveal xref-bibr">160</a>] descriptor and the drug molecules are encoded as fingerprint feature vector [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>].</td></tr><tr><td>DrugRPE</td><td></td><td>A random projection ensemble approach for based on the REPTree algorithm [<span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>] and using random projection [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>, <span class="xrefLink" id="jumplink-ref162"></span><a href="javascript:;" reveal-id="ref162" data-open="ref162" class="link link-ref link-reveal xref-bibr">162</a>, <span class="xrefLink" id="jumplink-ref163"></span><a href="javascript:;" reveal-id="ref163" data-open="ref163" class="link link-ref link-reveal xref-bibr">163</a>, <span class="xrefLink" id="jumplink-ref163 ref164 ref165"></span><a href="javascript:;" reveal-id="ref163 ref164 ref165" data-open="ref163 ref164 ref165" class="link link-ref link-reveal xref-bibr">163–165</a>].</td></tr><tr><td>CGBVS</td><td>ChemoGenomics-Based Virtual Screening</td><td>A kernel-based state-of-the-art method using virtual screening (VS) [<span class="xrefLink" id="jumplink-ref89"></span><a href="javascript:;" reveal-id="ref89" data-open="ref89" class="link link-ref link-reveal xref-bibr">89</a>] and pairwise kernel method (PKM) [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>] [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>].</td></tr><tr><td></td><td>DASPfind</td><td>A computational DTI prediction method relying on the topological structure of the heterogeneous graph interaction model [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>].</td></tr><tr><td>SAR</td><td>Structure-Activity Relationship method</td><td>A screening of chemical compounds method for classification problem of DTIs using protein sequences and drug topological structures [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>, <span class="xrefLink" id="jumplink-ref168"></span><a href="javascript:;" reveal-id="ref168" data-open="ref168" class="link link-ref link-reveal xref-bibr">168</a>].</td></tr><tr><td>DVM</td><td>Discriminative Vector Machine</td><td>A classifier and a method by formulating the DTIs as an extended SAR classification problem [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>] (using principal component analysis (PCA) method [<span class="xrefLink" id="jumplink-ref170"></span><a href="javascript:;" reveal-id="ref170" data-open="ref170" class="link link-ref link-reveal xref-bibr">170</a>]).</td></tr><tr><td>EnsL</td><td>Ensemble Learning (with dimensionality reduction, or class imbalance-aware)</td><td>A framework predicts DTI based on average voting of its base classifiers: Decision Tree (EnsemDT) [<span class="xrefLink" id="jumplink-ref171 ref172 ref173"></span><a href="javascript:;" reveal-id="ref171 ref172 ref173" data-open="ref171 ref172 ref173" class="link link-ref link-reveal xref-bibr">171–173</a>] (based on Singular Value Decomposition (SVD), Partial Least Squares (PLS) [<span class="xrefLink" id="jumplink-ref174"></span><a href="javascript:;" reveal-id="ref174" data-open="ref174" class="link link-ref link-reveal xref-bibr">174</a>] and Laplacian Eigenmaps (LapEig) [<span class="xrefLink" id="jumplink-ref175"></span><a href="javascript:;" reveal-id="ref175" data-open="ref175" class="link link-ref link-reveal xref-bibr">175</a>]),Kernel Ridge Regression (EnsemKRR), Random Forest (EnsemRF) [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>], stacked (EnsemSTACK) [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>], DrugE-Rank [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>].</td></tr><tr><td>BE-DTI</td><td>Bagging-based Ensemble method</td><td>A bagging-based ensemble framework that involves dimensionality reduction and active learning [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>].</td></tr></tbody></table></div></div></div> <a id="225532422" scrollto-destination="225532422"></a> <div data-id="f3" data-content-id="f3" class="fig fig-section js-fig-section" swap-content-for-modal="true"><div class="graphic-wrap"><img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/m_bbz157f3.jpeg?Expires=1734462476&Signature=qPcc1i5sYm7Z17QCf5a~WT11B204g5a9ajVMAd7hziK3bE8dQASCJwXxWSxOU12609Q9NZvyn8JeKKQgl--BIXO1Hh86KoIYGw56be9mQhGYnGuTsyOEg~Odal0Di8eW87kVZzPOtCDs8fhEvdWKgAYtb8GpBTyVbCXjByFRgnXXXNCRyDPe6q219CyX02wANQ6fqxfUC58MKJN3GvLgVqWSkH3pUffbB5UKlcSRhzHAYayuzDz~dkrza9VBJy1lalDNtXJpMz4lPhcp5YvV4TOPxpNx4uyTboZlJiK1BaVDJNQjcC31VXj1IgzuT4nCmO8JzsW~46gzoWGBWNUdxw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="Matrix factorization method." data-path-from-xml="bbz157f3.tif" /><div class="graphic-bottom"><div class="label fig-label" id="label-225532422"><strong>Figure 3</strong></div><div class="caption fig-caption"><p class="chapter-para">Matrix factorization method.</p></div><div class="ajax-articleAbstract-exclude-regex fig-orig original-slide figure-button-wrap"><a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" role="button" aria-describedby="label-225532422" href="/view-large/figure/225532422/bbz157f3.tif" data-path-from-xml="bbz157f3.tif" target="_blank">Open in new tab</a><a class="download-slide" role="button" aria-describedby="label-225532422" data-section="225532422" href="/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/bbz157f3.jpeg?Expires=1734462476&Signature=H2h3d4-6aX69zEYqeXgKH71q2aDFCKTE3hYtq8e0NPY-xSlH3LSjqtLzLAxhV-HlE~yzFdFlT8abDpw0OSVPVDrL33L2-UiCOkw2zlUWyelZZluH0080jzg96bPJftxzd5ElrdRrfu0CiM3725nofgzkgRzD7hcDozq7DoItqOKN~xCJODq3SoWpsAKfzG5W9cT7InlChDZIFIpPIiVG3kTFzUagrWP3-O1gBajXgPNv1DQhmiFazpFAWYUotK1Ujivp0-J7Ethj6ky7vlwjmjGBM6QT-Hqjoepm2WC9aWrRKXgGRSka-N2thi7XEpPrOyUEGLlTd3VdY1tE0pxSSw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=225532422&ar=5681786&xsltPath=~/UI/app/XSLT&imagename=&siteId=5143" data-path-from-xml="bbz157f3.tif">Download slide</a></div></div></div></div> <h3 scrollto-destination=225532423 id="225532423" class="section-title js-splitscreen-section-title" data-legacy-id=sec2e>2.5 Matrix factorization methods</h3> <div class="
 block-child-p
 ">The matrix factorization methods have been shown to outperform other groups of machine learning methods in the prediction of DTI. Given an interaction matrix <span class="inline-formula no-formula-id">|$X_{n\times m}$|⁠</span>, <div class="formula-wrap"><div class="disp-formula" id="jumplink-" content-id=""><div class="tex-math display-math">$$\begin{equation} X_{n\times m}= \begin{pmatrix} x_{11}& \cdots &x_{1m}\\ \vdots& \vdots&\vdots\\ x_{n1}& \cdots &x_{nm}, \end{pmatrix} \notag \end{equation}$$</div></div></div> for <span class="inline-formula no-formula-id">|$i=1:n$|</span> and <span class="inline-formula no-formula-id">|$j=1:m$|⁠</span>, one may define <div class="formula-wrap"><div class="disp-formula" id="jumplink-" content-id=""><div class="tex-math display-math">$$\begin{align*} x_{ij} & =\begin{cases} 1 & \textrm{if drug}\ d_{i}\ \textrm{and target}\ t_{j}\ \textrm{interact}\\ 0 & \textrm{in the absence of any known interaction} \end{cases} \end{align*}$$</div></div></div>the primarily goal in DTI prediction is to decompose matrix <span class="inline-formula no-formula-id">|$X_{n\times m}$|</span> into two matrices, <span class="inline-formula no-formula-id">|$Y_{n\times k}$|</span> and <span class="inline-formula no-formula-id">|$Z_{m\times k}$|⁠</span>, where <span class="inline-formula no-formula-id">|$X\simeq Y Z^{T}$|</span> with <span class="inline-formula no-formula-id">|$k<n,m$|</span> (Figure <span class="xrefLink" id="jumplink-f3"></span><a href="javascript:;" data-modal-source-id="f3" class="link xref-fig">3</a>). Here <span class="inline-formula no-formula-id">|$Z^T$|</span> denotes the transposed matrix of <span class="inline-formula no-formula-id">|$Z$|⁠</span>. This will factorize matrix <span class="inline-formula no-formula-id">|$X_{n\times m}$|</span> into two matrices with lower orders (i.e. rank reduction), which make it easier to perform the matrix completion techniques in order to handle the missing data.</div><p class="chapter-para">In contrast to most machine learning methods used for DTI prediction that need (2D) drug structural similarities, certain matrix factorization methods do not rely on chemical similarity or drug similarities and instead utilize collaborative filtering algorithms, among which one could name probabilistic matrix factorization (PMF) [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>]. Some other methods are inspired by the idea of low-rank embedding (LRE) [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>, <span class="xrefLink" id="jumplink-ref181"></span><a href="javascript:;" reveal-id="ref181" data-open="ref181" class="link link-ref link-reveal xref-bibr">181</a>] with the goal of finding a low-rank representation <span class="inline-formula no-formula-id">|$R$|</span> of the dataset <span class="inline-formula no-formula-id">|$X$|</span> by an optimization problem and then fixing <span class="inline-formula no-formula-id">|$R$|</span> and minimizing the reconstruction error in the embedded space in a way that the pointwise linear reconstruction (local structure of original samples) is preserved.</p><p class="chapter-para">In this group of methods, it is assumed that the drugs and targets are lying in the same distance space such that the distance among drugs and targets can be used to measure the strength of their interactions. Therefore, both drugs and targets can be embedded in a common low-dimensional subspace with some constraints.</p><p class="chapter-para">Although this group of methods has been shown to be more reliable than the others, rapid growth in the quantity and variety of data related to a certain drug and/or a target far exceeds the capacity of matrix-based data representations and many current analysis algorithms. A solution to this issue has been proposed in Section <span class="xrefLink" id="jumplink-sec4"></span><a href="#sec4" class="sectionLink xref-sec js-xref-sec">4</a>. In Table <span class="xrefLink" id="jumplink-TB5"></span><a href="javascript:;" reveal-id="TB5" data-open="TB5" class="link link-reveal link-table xref-fig">5</a>, the matrix factorization methods and the paper(s) in which they are proposed, developed and employed are listed.</p> <a id="225532428" scrollto-destination="225532428"></a> <div content-id="TB5" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB5" data-id="TB5"><span class="label title-label" id="label-26185">Table 5</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532428" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Matrix factorization methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>MSCMF</td><td>Multiple Similarities one-Class Matrix Factorization</td><td>An approach to approximate the input DTI matrix by two low-rank matrices, which share the same feature space and are generated by the weighted similarity matrices of drugs and those of targets, respectively [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>] using [<span class="xrefLink" id="jumplink-ref183 ref184 ref185 ref186"></span><a href="javascript:;" reveal-id="ref183 ref184 ref185 ref186" data-open="ref183 ref184 ref185 ref186" class="link link-ref link-reveal xref-bibr">183–186</a>].</td></tr><tr><td>NRLMF</td><td>Neighborhood Regularized Logistic Matrix Factorization</td><td>A mode that integrates logistic matrix factorization with neighborhood regularization for DTI prediction [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>].</td></tr><tr><td>PMF</td><td>Probabilistic Matrix Factorization</td><td>A collaborative filtering method that decomposes the DT bipartite connectivity matrix as a product of two matrices of latent variables that will be used for prediction, irrespective of the drug or target similarities [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>].</td></tr><tr><td>DLGRMC</td><td>Dual Laplacian Graph Regularized Matrix Completion</td><td>An optimization framework for low-rank approximation of interaction matrix based on matrix completion in which drug similarity and target similarity are used as dual Laplacian graph regularization term [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>].</td></tr><tr><td>GRMF-WGRMF</td><td>Graph Regularized Matrix Factorization and Weighted GRMF</td><td>Two manifold learners for extracting low-dimensional non-linear manifolds of DTI bipartite graph [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>].</td></tr><tr><td>Pseudo-SMR</td><td>Pseudo Substitution Matrix Representation</td><td>An extension to SAR classification problem[<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>], employing a python package called <a class="link link-uri openInAnotherWindow" href="http://scikit-learn" target="_blank">scikit-learn</a> for machine learning to implement Extremely Randomized Tree (ER-Tree) introduced in [<span class="xrefLink" id="jumplink-ref190"></span><a href="javascript:;" reveal-id="ref190" data-open="ref190" class="link link-ref link-reveal xref-bibr">190</a>, <span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>].</td></tr><tr><td>BRDTI</td><td>Bayesian Ranking method</td><td>A method based on Bayesian Personalized Ranking matrix factorization (BPR) that incorporates target bias and content alignment for drug and target similarities [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>, <span class="xrefLink" id="jumplink-ref192"></span><a href="javascript:;" reveal-id="ref192" data-open="ref192" class="link link-ref link-reveal xref-bibr">192</a>].</td></tr><tr><td>LRE, SLRE, MLRE</td><td>Low Rank Embedding</td><td>An algorithm of finding a low-rank representation (by optimization problem) and fixing and minimizing the reconstruction error in th embedded space in a way that the pointwise linear reconstruction (local structure of original samples) is preserved [<span class="xrefLink" id="jumplink-ref181"></span><a href="javascript:;" reveal-id="ref181" data-open="ref181" class="link link-ref link-reveal xref-bibr">181</a>]. LRE for an arbitrary view (structure or chemical) is called SLRE and for multiview is called MLRE [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>].</td></tr><tr><td>VB-MK-LMF</td><td>Variational Bayesian Multiple Kernel Logistic Matrix Factorization</td><td>A method integrating multiple kernel learning, weighted observations, graph Laplacian regularization and explicit modeling of probabilities of binary DTIs [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>].</td></tr><tr><td>KBMF, KBMF2K</td><td>Kernelized Bayesian Matrix Factorization</td><td>A method for factorizing the interaction score matrix in terms of kernel matrices (similarity matrices), which can be used as DTI predictors for new drugs and protein KBMF2K [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>MSCMF</td><td>Multiple Similarities one-Class Matrix Factorization</td><td>An approach to approximate the input DTI matrix by two low-rank matrices, which share the same feature space and are generated by the weighted similarity matrices of drugs and those of targets, respectively [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>] using [<span class="xrefLink" id="jumplink-ref183 ref184 ref185 ref186"></span><a href="javascript:;" reveal-id="ref183 ref184 ref185 ref186" data-open="ref183 ref184 ref185 ref186" class="link link-ref link-reveal xref-bibr">183–186</a>].</td></tr><tr><td>NRLMF</td><td>Neighborhood Regularized Logistic Matrix Factorization</td><td>A mode that integrates logistic matrix factorization with neighborhood regularization for DTI prediction [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>].</td></tr><tr><td>PMF</td><td>Probabilistic Matrix Factorization</td><td>A collaborative filtering method that decomposes the DT bipartite connectivity matrix as a product of two matrices of latent variables that will be used for prediction, irrespective of the drug or target similarities [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>].</td></tr><tr><td>DLGRMC</td><td>Dual Laplacian Graph Regularized Matrix Completion</td><td>An optimization framework for low-rank approximation of interaction matrix based on matrix completion in which drug similarity and target similarity are used as dual Laplacian graph regularization term [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>].</td></tr><tr><td>GRMF-WGRMF</td><td>Graph Regularized Matrix Factorization and Weighted GRMF</td><td>Two manifold learners for extracting low-dimensional non-linear manifolds of DTI bipartite graph [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>].</td></tr><tr><td>Pseudo-SMR</td><td>Pseudo Substitution Matrix Representation</td><td>An extension to SAR classification problem[<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>], employing a python package called <a class="link link-uri openInAnotherWindow" href="http://scikit-learn" target="_blank">scikit-learn</a> for machine learning to implement Extremely Randomized Tree (ER-Tree) introduced in [<span class="xrefLink" id="jumplink-ref190"></span><a href="javascript:;" reveal-id="ref190" data-open="ref190" class="link link-ref link-reveal xref-bibr">190</a>, <span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>].</td></tr><tr><td>BRDTI</td><td>Bayesian Ranking method</td><td>A method based on Bayesian Personalized Ranking matrix factorization (BPR) that incorporates target bias and content alignment for drug and target similarities [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>, <span class="xrefLink" id="jumplink-ref192"></span><a href="javascript:;" reveal-id="ref192" data-open="ref192" class="link link-ref link-reveal xref-bibr">192</a>].</td></tr><tr><td>LRE, SLRE, MLRE</td><td>Low Rank Embedding</td><td>An algorithm of finding a low-rank representation (by optimization problem) and fixing and minimizing the reconstruction error in th embedded space in a way that the pointwise linear reconstruction (local structure of original samples) is preserved [<span class="xrefLink" id="jumplink-ref181"></span><a href="javascript:;" reveal-id="ref181" data-open="ref181" class="link link-ref link-reveal xref-bibr">181</a>]. LRE for an arbitrary view (structure or chemical) is called SLRE and for multiview is called MLRE [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>].</td></tr><tr><td>VB-MK-LMF</td><td>Variational Bayesian Multiple Kernel Logistic Matrix Factorization</td><td>A method integrating multiple kernel learning, weighted observations, graph Laplacian regularization and explicit modeling of probabilities of binary DTIs [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>].</td></tr><tr><td>KBMF, KBMF2K</td><td>Kernelized Bayesian Matrix Factorization</td><td>A method for factorizing the interaction score matrix in terms of kernel matrices (similarity matrices), which can be used as DTI predictors for new drugs and protein KBMF2K [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>].</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB5" data-id="TB5"><span class="label title-label" id="label-26185">Table 5</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532428" aria-describedby="label-26185"> Open in new tab </a></div><div class="caption caption-id-" id="caption-26185"><p class="chapter-para">Matrix factorization methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-26185" aria-describedby="
 caption-26185"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>MSCMF</td><td>Multiple Similarities one-Class Matrix Factorization</td><td>An approach to approximate the input DTI matrix by two low-rank matrices, which share the same feature space and are generated by the weighted similarity matrices of drugs and those of targets, respectively [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>] using [<span class="xrefLink" id="jumplink-ref183 ref184 ref185 ref186"></span><a href="javascript:;" reveal-id="ref183 ref184 ref185 ref186" data-open="ref183 ref184 ref185 ref186" class="link link-ref link-reveal xref-bibr">183–186</a>].</td></tr><tr><td>NRLMF</td><td>Neighborhood Regularized Logistic Matrix Factorization</td><td>A mode that integrates logistic matrix factorization with neighborhood regularization for DTI prediction [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>].</td></tr><tr><td>PMF</td><td>Probabilistic Matrix Factorization</td><td>A collaborative filtering method that decomposes the DT bipartite connectivity matrix as a product of two matrices of latent variables that will be used for prediction, irrespective of the drug or target similarities [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>].</td></tr><tr><td>DLGRMC</td><td>Dual Laplacian Graph Regularized Matrix Completion</td><td>An optimization framework for low-rank approximation of interaction matrix based on matrix completion in which drug similarity and target similarity are used as dual Laplacian graph regularization term [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>].</td></tr><tr><td>GRMF-WGRMF</td><td>Graph Regularized Matrix Factorization and Weighted GRMF</td><td>Two manifold learners for extracting low-dimensional non-linear manifolds of DTI bipartite graph [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>].</td></tr><tr><td>Pseudo-SMR</td><td>Pseudo Substitution Matrix Representation</td><td>An extension to SAR classification problem[<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>], employing a python package called <a class="link link-uri openInAnotherWindow" href="http://scikit-learn" target="_blank">scikit-learn</a> for machine learning to implement Extremely Randomized Tree (ER-Tree) introduced in [<span class="xrefLink" id="jumplink-ref190"></span><a href="javascript:;" reveal-id="ref190" data-open="ref190" class="link link-ref link-reveal xref-bibr">190</a>, <span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>].</td></tr><tr><td>BRDTI</td><td>Bayesian Ranking method</td><td>A method based on Bayesian Personalized Ranking matrix factorization (BPR) that incorporates target bias and content alignment for drug and target similarities [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>, <span class="xrefLink" id="jumplink-ref192"></span><a href="javascript:;" reveal-id="ref192" data-open="ref192" class="link link-ref link-reveal xref-bibr">192</a>].</td></tr><tr><td>LRE, SLRE, MLRE</td><td>Low Rank Embedding</td><td>An algorithm of finding a low-rank representation (by optimization problem) and fixing and minimizing the reconstruction error in th embedded space in a way that the pointwise linear reconstruction (local structure of original samples) is preserved [<span class="xrefLink" id="jumplink-ref181"></span><a href="javascript:;" reveal-id="ref181" data-open="ref181" class="link link-ref link-reveal xref-bibr">181</a>]. LRE for an arbitrary view (structure or chemical) is called SLRE and for multiview is called MLRE [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>].</td></tr><tr><td>VB-MK-LMF</td><td>Variational Bayesian Multiple Kernel Logistic Matrix Factorization</td><td>A method integrating multiple kernel learning, weighted observations, graph Laplacian regularization and explicit modeling of probabilities of binary DTIs [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>].</td></tr><tr><td>KBMF, KBMF2K</td><td>Kernelized Bayesian Matrix Factorization</td><td>A method for factorizing the interaction score matrix in terms of kernel matrices (similarity matrices), which can be used as DTI predictors for new drugs and protein KBMF2K [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>MSCMF</td><td>Multiple Similarities one-Class Matrix Factorization</td><td>An approach to approximate the input DTI matrix by two low-rank matrices, which share the same feature space and are generated by the weighted similarity matrices of drugs and those of targets, respectively [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>] using [<span class="xrefLink" id="jumplink-ref183 ref184 ref185 ref186"></span><a href="javascript:;" reveal-id="ref183 ref184 ref185 ref186" data-open="ref183 ref184 ref185 ref186" class="link link-ref link-reveal xref-bibr">183–186</a>].</td></tr><tr><td>NRLMF</td><td>Neighborhood Regularized Logistic Matrix Factorization</td><td>A mode that integrates logistic matrix factorization with neighborhood regularization for DTI prediction [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>].</td></tr><tr><td>PMF</td><td>Probabilistic Matrix Factorization</td><td>A collaborative filtering method that decomposes the DT bipartite connectivity matrix as a product of two matrices of latent variables that will be used for prediction, irrespective of the drug or target similarities [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>].</td></tr><tr><td>DLGRMC</td><td>Dual Laplacian Graph Regularized Matrix Completion</td><td>An optimization framework for low-rank approximation of interaction matrix based on matrix completion in which drug similarity and target similarity are used as dual Laplacian graph regularization term [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>].</td></tr><tr><td>GRMF-WGRMF</td><td>Graph Regularized Matrix Factorization and Weighted GRMF</td><td>Two manifold learners for extracting low-dimensional non-linear manifolds of DTI bipartite graph [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>].</td></tr><tr><td>Pseudo-SMR</td><td>Pseudo Substitution Matrix Representation</td><td>An extension to SAR classification problem[<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>], employing a python package called <a class="link link-uri openInAnotherWindow" href="http://scikit-learn" target="_blank">scikit-learn</a> for machine learning to implement Extremely Randomized Tree (ER-Tree) introduced in [<span class="xrefLink" id="jumplink-ref190"></span><a href="javascript:;" reveal-id="ref190" data-open="ref190" class="link link-ref link-reveal xref-bibr">190</a>, <span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>].</td></tr><tr><td>BRDTI</td><td>Bayesian Ranking method</td><td>A method based on Bayesian Personalized Ranking matrix factorization (BPR) that incorporates target bias and content alignment for drug and target similarities [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>, <span class="xrefLink" id="jumplink-ref99"></span><a href="javascript:;" reveal-id="ref99" data-open="ref99" class="link link-ref link-reveal xref-bibr">99</a>, <span class="xrefLink" id="jumplink-ref192"></span><a href="javascript:;" reveal-id="ref192" data-open="ref192" class="link link-ref link-reveal xref-bibr">192</a>].</td></tr><tr><td>LRE, SLRE, MLRE</td><td>Low Rank Embedding</td><td>An algorithm of finding a low-rank representation (by optimization problem) and fixing and minimizing the reconstruction error in th embedded space in a way that the pointwise linear reconstruction (local structure of original samples) is preserved [<span class="xrefLink" id="jumplink-ref181"></span><a href="javascript:;" reveal-id="ref181" data-open="ref181" class="link link-ref link-reveal xref-bibr">181</a>]. LRE for an arbitrary view (structure or chemical) is called SLRE and for multiview is called MLRE [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>].</td></tr><tr><td>VB-MK-LMF</td><td>Variational Bayesian Multiple Kernel Logistic Matrix Factorization</td><td>A method integrating multiple kernel learning, weighted observations, graph Laplacian regularization and explicit modeling of probabilities of binary DTIs [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>].</td></tr><tr><td>KBMF, KBMF2K</td><td>Kernelized Bayesian Matrix Factorization</td><td>A method for factorizing the interaction score matrix in terms of kernel matrices (similarity matrices), which can be used as DTI predictors for new drugs and protein KBMF2K [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>].</td></tr></tbody></table></div></div></div> <h3 scrollto-destination=225532429 id="225532429" class="section-title js-splitscreen-section-title" data-legacy-id=sec2f>2.6 Network-based methods</h3> <p class="chapter-para">The network-based methods refer to those that utilize graph-based techniques in order to perform the task of DTI prediction (Figure <span class="xrefLink" id="jumplink-f4"></span><a href="javascript:;" data-modal-source-id="f4" class="link xref-fig">4</a>). Among the methods is network-based inference (NBI) for DTI prediction, which is among the simplest yet most reliable inference methods and uses only DT bipartite network topology similarity [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>].</p> <a id="225532431" scrollto-destination="225532431"></a> <div data-id="f4" data-content-id="f4" class="fig fig-section js-fig-section" swap-content-for-modal="true"><div class="graphic-wrap"><img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/m_bbz157f4.jpeg?Expires=1734462476&Signature=0OdGfLioYXXZDkRAAWP9RTy5AdcQmIvSK5u9viRwDT81r5uccMiAds8HsodtTdcC-RsHTPf3RT7Wyl95Uz~zhnLs8pR0yqWNqtzCOLsGPzFvGBOvoVEEt49MDCgqxo4Zr8-8s5bbsV6cxov90pHwUTAglMXm6XXlV-01wIISN6~zd~5HjwcO6~hfqSYqJeSUqiJ1MJQb6kyiCbHnsVOO-dWxntKxjK-cHezGZ2NwkyHRE5wOlBaTrO05VVwunAiHZV7F5uRbv-PFDDb0TC5wRPByy6i97U6S2MlFScVJjxHe1Nmx1aqaykKnFb7x93xc400AjVql0738ZlFto4IO1w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="Drug–target interaction heterogeneous network." data-path-from-xml="bbz157f4.tif" /><div class="graphic-bottom"><div class="label fig-label" id="label-225532431"><strong>Figure 4</strong></div><div class="caption fig-caption"><p class="chapter-para">Drug–target interaction heterogeneous network.</p></div><div class="ajax-articleAbstract-exclude-regex fig-orig original-slide figure-button-wrap"><a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" role="button" aria-describedby="label-225532431" href="/view-large/figure/225532431/bbz157f4.tif" data-path-from-xml="bbz157f4.tif" target="_blank">Open in new tab</a><a class="download-slide" role="button" aria-describedby="label-225532431" data-section="225532431" href="/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/bbz157f4.jpeg?Expires=1734462476&Signature=j~kDVS4W-k~wnoWzeaIU6-1sIiBEkVbnqd-Dt3HnxSFaZPQYstjk14AVeeuEPrQ1OGE-tuGQnozjKkQz4Bv4quFBhzqLZkCmkh22-qPGYO1MHOfzP1DlKp~f5MHBF2v3Vh7m2v7TVs~Y1a5aBN-c6pIlYLGndPYMempXu-WjvPHgnaIRJf0Mhnbmdn7m9T3WdWuMvx0u2qdIMyxlHFc0p~cI0NCA-R4Ry2yX~m6jOSqtSa6F2GK93-FahmRmkqX04L4asjAOi29cA0HTELtoQQ~elXq8POTJsN3MbYyl9L6WHI5wZEjmtKXouo0DMpVkpov6QFXOIjkof58kzNrvDA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=225532431&ar=5681786&xsltPath=~/UI/app/XSLT&imagename=&siteId=5143" data-path-from-xml="bbz157f4.tif">Download slide</a></div></div></div></div><p class="chapter-para">Moreover, in certain methods three networks of protein–protein similarity, drug–drug similarity and known DTIs are integrated into a heterogeneous network and assumed similar drugs often target similar proteins [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>, <span class="xrefLink" id="jumplink-ref203"></span><a href="javascript:;" reveal-id="ref203" data-open="ref203" class="link link-ref link-reveal xref-bibr">203</a>]. A two-layer undirected graphical representation of the network could also be adopted in order to train to predict direct DTIs (usually caused by protein–ligand binding), indirect DTIs and drug mode of actions (binding interaction, activation interaction and inhibition interaction) in addition to performing the DTI prediction task. A pertinent example is proposed in [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>] using Restricted Boltzmann Machine (RBM) [<span class="xrefLink" id="jumplink-ref123"></span><a href="javascript:;" reveal-id="ref123" data-open="ref123" class="link link-ref link-reveal xref-bibr">123</a>]. A list of network-based methods with a short description for each method is provided in Table <span class="xrefLink" id="jumplink-TB6"></span><a href="javascript:;" reveal-id="TB6" data-open="TB6" class="link link-reveal link-table xref-fig">6</a>.</p> <a id="225532433" scrollto-destination="225532433"></a> <div content-id="TB6" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB6" data-id="TB6"><span class="label title-label" id="label-97946">Table 6</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532433" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">Network-based methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>NBI</td><td>Network-Based Inference</td><td>A method based on DT bipartite network topology similarity [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>].</td></tr><tr><td>NRWRH</td><td>Network-based Random Walk with Restart on the Heterogeneous network</td><td>A method based on the framework of RWR to infer potential DTIs on a bipartite graph network [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>].</td></tr><tr><td>NetCBP</td><td>Network-Consistency-based Prediction Method</td><td>A semi-supervised inference method, utilizing both labeled and unlabeled data [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>].</td></tr><tr><td>DTINet</td><td></td><td>A computational network integration pipeline for DTI prediction [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>].</td></tr><tr><td>IN-RWR</td><td>inter/intra-network RWR or Co-rank</td><td>Two network prediction methods based on Co-rank algorithm that involves RWR on bipartite graph [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>].</td></tr><tr><td></td><td>NormMulInf</td><td>A method based on collaborative filtering that incorporates multiple available data sources related to drugs and targets can improve DTI prediction performance [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>] using robust PCA [<span class="xrefLink" id="jumplink-ref200"></span><a href="javascript:;" reveal-id="ref200" data-open="ref200" class="link link-ref link-reveal xref-bibr">200</a>].</td></tr><tr><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>Beta-distribution-rescored NRLMF</td><td>An improved NRLMF algorithm that rescores the score of NRLMF as the expected value of the <span class="inline-formula no-formula-id">|$\beta $|</span>-distribution, which is determined based on interaction information and NRLMF score. [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>].</td></tr><tr><td>RWR</td><td>Random Walk with Restart</td><td>A method that requires a matrix inversion and provides a good relevance score between two nodes in a weighted graph of DTIs [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>NBI</td><td>Network-Based Inference</td><td>A method based on DT bipartite network topology similarity [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>].</td></tr><tr><td>NRWRH</td><td>Network-based Random Walk with Restart on the Heterogeneous network</td><td>A method based on the framework of RWR to infer potential DTIs on a bipartite graph network [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>].</td></tr><tr><td>NetCBP</td><td>Network-Consistency-based Prediction Method</td><td>A semi-supervised inference method, utilizing both labeled and unlabeled data [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>].</td></tr><tr><td>DTINet</td><td></td><td>A computational network integration pipeline for DTI prediction [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>].</td></tr><tr><td>IN-RWR</td><td>inter/intra-network RWR or Co-rank</td><td>Two network prediction methods based on Co-rank algorithm that involves RWR on bipartite graph [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>].</td></tr><tr><td></td><td>NormMulInf</td><td>A method based on collaborative filtering that incorporates multiple available data sources related to drugs and targets can improve DTI prediction performance [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>] using robust PCA [<span class="xrefLink" id="jumplink-ref200"></span><a href="javascript:;" reveal-id="ref200" data-open="ref200" class="link link-ref link-reveal xref-bibr">200</a>].</td></tr><tr><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>Beta-distribution-rescored NRLMF</td><td>An improved NRLMF algorithm that rescores the score of NRLMF as the expected value of the <span class="inline-formula no-formula-id">|$\beta $|</span>-distribution, which is determined based on interaction information and NRLMF score. [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>].</td></tr><tr><td>RWR</td><td>Random Walk with Restart</td><td>A method that requires a matrix inversion and provides a good relevance score between two nodes in a weighted graph of DTIs [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>].</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB6" data-id="TB6"><span class="label title-label" id="label-97946">Table 6</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532433" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">Network-based methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>NBI</td><td>Network-Based Inference</td><td>A method based on DT bipartite network topology similarity [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>].</td></tr><tr><td>NRWRH</td><td>Network-based Random Walk with Restart on the Heterogeneous network</td><td>A method based on the framework of RWR to infer potential DTIs on a bipartite graph network [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>].</td></tr><tr><td>NetCBP</td><td>Network-Consistency-based Prediction Method</td><td>A semi-supervised inference method, utilizing both labeled and unlabeled data [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>].</td></tr><tr><td>DTINet</td><td></td><td>A computational network integration pipeline for DTI prediction [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>].</td></tr><tr><td>IN-RWR</td><td>inter/intra-network RWR or Co-rank</td><td>Two network prediction methods based on Co-rank algorithm that involves RWR on bipartite graph [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>].</td></tr><tr><td></td><td>NormMulInf</td><td>A method based on collaborative filtering that incorporates multiple available data sources related to drugs and targets can improve DTI prediction performance [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>] using robust PCA [<span class="xrefLink" id="jumplink-ref200"></span><a href="javascript:;" reveal-id="ref200" data-open="ref200" class="link link-ref link-reveal xref-bibr">200</a>].</td></tr><tr><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>Beta-distribution-rescored NRLMF</td><td>An improved NRLMF algorithm that rescores the score of NRLMF as the expected value of the <span class="inline-formula no-formula-id">|$\beta $|</span>-distribution, which is determined based on interaction information and NRLMF score. [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>].</td></tr><tr><td>RWR</td><td>Random Walk with Restart</td><td>A method that requires a matrix inversion and provides a good relevance score between two nodes in a weighted graph of DTIs [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>NBI</td><td>Network-Based Inference</td><td>A method based on DT bipartite network topology similarity [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>].</td></tr><tr><td>NRWRH</td><td>Network-based Random Walk with Restart on the Heterogeneous network</td><td>A method based on the framework of RWR to infer potential DTIs on a bipartite graph network [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>].</td></tr><tr><td>NetCBP</td><td>Network-Consistency-based Prediction Method</td><td>A semi-supervised inference method, utilizing both labeled and unlabeled data [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>].</td></tr><tr><td>DTINet</td><td></td><td>A computational network integration pipeline for DTI prediction [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>].</td></tr><tr><td>IN-RWR</td><td>inter/intra-network RWR or Co-rank</td><td>Two network prediction methods based on Co-rank algorithm that involves RWR on bipartite graph [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>].</td></tr><tr><td></td><td>NormMulInf</td><td>A method based on collaborative filtering that incorporates multiple available data sources related to drugs and targets can improve DTI prediction performance [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>] using robust PCA [<span class="xrefLink" id="jumplink-ref200"></span><a href="javascript:;" reveal-id="ref200" data-open="ref200" class="link link-ref link-reveal xref-bibr">200</a>].</td></tr><tr><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>Beta-distribution-rescored NRLMF</td><td>An improved NRLMF algorithm that rescores the score of NRLMF as the expected value of the <span class="inline-formula no-formula-id">|$\beta $|</span>-distribution, which is determined based on interaction information and NRLMF score. [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>].</td></tr><tr><td>RWR</td><td>Random Walk with Restart</td><td>A method that requires a matrix inversion and provides a good relevance score between two nodes in a weighted graph of DTIs [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>].</td></tr></tbody></table></div></div></div> <h3 scrollto-destination=225532434 id="225532434" class="section-title js-splitscreen-section-title" data-legacy-id=sec2g>2.7 Hybrid methods</h3> <p class="chapter-para">Hybrid methods refer to all the approaches in which any combination of the feature-based, matrix factorization, deep learning and network-based methods are exploited. This can extend the capability of a prediction algorithm by integrating different sets of information. The hybrid methods in general serve two purposes; they address the problems of unknown interaction in DTIs as well as taking the most advantage of machine learning methods, simultaneously. For instance, authors in [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>] proposed a method integrating feature-based and similarity-based machine learning approaches [<span class="xrefLink" id="jumplink-ref205"></span><a href="javascript:;" reveal-id="ref205" data-open="ref205" class="link link-ref link-reveal xref-bibr">205</a>, <span class="xrefLink" id="jumplink-ref206"></span><a href="javascript:;" reveal-id="ref206" data-open="ref206" class="link link-ref link-reveal xref-bibr">206</a>]. The hybrid methods performed superior to other state-of-the-art methods by optimizing the feature extraction process by extracting the complex hidden features of drugs and targets [<span class="xrefLink" id="jumplink-ref134"></span><a href="javascript:;" reveal-id="ref134" data-open="ref134" class="link link-ref link-reveal xref-bibr">134</a>, <span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>, <span class="xrefLink" id="jumplink-ref172"></span><a href="javascript:;" reveal-id="ref172" data-open="ref172" class="link link-ref link-reveal xref-bibr">172</a>, <span class="xrefLink" id="jumplink-ref173"></span><a href="javascript:;" reveal-id="ref173" data-open="ref173" class="link link-ref link-reveal xref-bibr">173</a>, <span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>, <span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>, <span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>, <span class="xrefLink" id="jumplink-ref207"></span><a href="javascript:;" reveal-id="ref207" data-open="ref207" class="link link-ref link-reveal xref-bibr">207</a>, <span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>]. Integrating two machine learning methods in DTI prediction often has a leverage in terms of results as they fully exploit the potential of two methods, simultaneously. However, one should be able to deal with the high complexity (either computational or operational) caused by integrating two groups of methods. A short description of such methods are listed in Table <span class="xrefLink" id="jumplink-TB7"></span><a href="javascript:;" reveal-id="TB7" data-open="TB7" class="link link-reveal link-table xref-fig">7</a>.</p> <a id="225532436" scrollto-destination="225532436"></a> <div content-id="TB7" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB7" data-id="TB7"><span class="label title-label" id="label-97946">Table 7</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532436" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">Hybrid methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DT-Hybrid</td><td>Domain tuned-hybrid</td><td>An extended NBI technique that incorporates domain-based knowledge such as drug similarities and target similarities [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>] (also look [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>?? ] for extension of the capability of recommender systems).</td></tr><tr><td>KMDR</td><td>Kernel Matrix Dimension Reduction</td><td>A framework for construction of link similarity matrix from kernel matrix and feature transformation for DTI prediction [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>].</td></tr><tr><td>MGRNNM, DGRMC</td><td>Multi Graph Regularized Nuclear Norm Minimization</td><td>A computational method that adds multiple drug–graph and target–graph Laplacian regularization terms to the standard matrix completion framework to predict DTIs [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>].</td></tr><tr><td>WLNM</td><td>Weisfeiler-Lehman Neural Machine</td><td>An algorithm for extraction of the adjacency matrix that represents the interactions between potential drugs and targets [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>].</td></tr><tr><td>PDTPS</td><td>Predicting Drug Targets with Protein Sequence</td><td>A framework based on Relevance Vector Machine that integrates Bi-gram probabilities, PSSM and PCA [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>].</td></tr><tr><td></td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized Classifier</td><td>A regularized classifiers over the tensor product space of DT pairs for extracting informative and biologically meaningful features for DTI prediction [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>].</td></tr><tr><td>RBM</td><td>Restricted Boltzmann Machine</td><td>A two-layer undirected graphical model to represent a multidimensional DTI network and encode different types of DTIs [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>].</td></tr><tr><td>LRF-DTI</td><td>Lasso-based Random Forest method</td><td>A method of DTI prediction based on Lasso dimensionality reduction and random forest predictor [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>].</td></tr><tr><td>COSINE</td><td>COld Start INtEractions</td><td>A statistical dual-regularized, one-class collaborative filtering method [<span class="xrefLink" id="jumplink-ref217"></span><a href="javascript:;" reveal-id="ref217" data-open="ref217" class="link link-ref link-reveal xref-bibr">217</a>] framework and a corresponding computational method for multi-target virtual screening using one-class collaborative filtering technique that can employ either logistic or linear factorization [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>].</td></tr><tr><td>DMF</td><td>Deep Matrix Factorization</td><td>A deep learning approach in the context of recommendation systems to extract the non-linearity of latent variables [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>] (DMF was originally introduced in [<span class="xrefLink" id="jumplink-ref220"></span><a href="javascript:;" reveal-id="ref220" data-open="ref220" class="link link-ref link-reveal xref-bibr">220</a>] as a deep learning method in the context of recommendation systems to extract the non-linearity of latent variables).</td></tr><tr><td>CoDe-DTI</td><td>COllaborative DEep learning-based DTI predictor</td><td>A method using both PMF and a denoising autoencoder [<span class="xrefLink" id="jumplink-ref221"></span><a href="javascript:;" reveal-id="ref221" data-open="ref221" class="link link-ref link-reveal xref-bibr">221</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DT-Hybrid</td><td>Domain tuned-hybrid</td><td>An extended NBI technique that incorporates domain-based knowledge such as drug similarities and target similarities [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>] (also look [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>?? ] for extension of the capability of recommender systems).</td></tr><tr><td>KMDR</td><td>Kernel Matrix Dimension Reduction</td><td>A framework for construction of link similarity matrix from kernel matrix and feature transformation for DTI prediction [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>].</td></tr><tr><td>MGRNNM, DGRMC</td><td>Multi Graph Regularized Nuclear Norm Minimization</td><td>A computational method that adds multiple drug–graph and target–graph Laplacian regularization terms to the standard matrix completion framework to predict DTIs [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>].</td></tr><tr><td>WLNM</td><td>Weisfeiler-Lehman Neural Machine</td><td>An algorithm for extraction of the adjacency matrix that represents the interactions between potential drugs and targets [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>].</td></tr><tr><td>PDTPS</td><td>Predicting Drug Targets with Protein Sequence</td><td>A framework based on Relevance Vector Machine that integrates Bi-gram probabilities, PSSM and PCA [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>].</td></tr><tr><td></td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized Classifier</td><td>A regularized classifiers over the tensor product space of DT pairs for extracting informative and biologically meaningful features for DTI prediction [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>].</td></tr><tr><td>RBM</td><td>Restricted Boltzmann Machine</td><td>A two-layer undirected graphical model to represent a multidimensional DTI network and encode different types of DTIs [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>].</td></tr><tr><td>LRF-DTI</td><td>Lasso-based Random Forest method</td><td>A method of DTI prediction based on Lasso dimensionality reduction and random forest predictor [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>].</td></tr><tr><td>COSINE</td><td>COld Start INtEractions</td><td>A statistical dual-regularized, one-class collaborative filtering method [<span class="xrefLink" id="jumplink-ref217"></span><a href="javascript:;" reveal-id="ref217" data-open="ref217" class="link link-ref link-reveal xref-bibr">217</a>] framework and a corresponding computational method for multi-target virtual screening using one-class collaborative filtering technique that can employ either logistic or linear factorization [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>].</td></tr><tr><td>DMF</td><td>Deep Matrix Factorization</td><td>A deep learning approach in the context of recommendation systems to extract the non-linearity of latent variables [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>] (DMF was originally introduced in [<span class="xrefLink" id="jumplink-ref220"></span><a href="javascript:;" reveal-id="ref220" data-open="ref220" class="link link-ref link-reveal xref-bibr">220</a>] as a deep learning method in the context of recommendation systems to extract the non-linearity of latent variables).</td></tr><tr><td>CoDe-DTI</td><td>COllaborative DEep learning-based DTI predictor</td><td>A method using both PMF and a denoising autoencoder [<span class="xrefLink" id="jumplink-ref221"></span><a href="javascript:;" reveal-id="ref221" data-open="ref221" class="link link-ref link-reveal xref-bibr">221</a>].</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB7" data-id="TB7"><span class="label title-label" id="label-97946">Table 7</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532436" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">Hybrid methods</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DT-Hybrid</td><td>Domain tuned-hybrid</td><td>An extended NBI technique that incorporates domain-based knowledge such as drug similarities and target similarities [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>] (also look [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>?? ] for extension of the capability of recommender systems).</td></tr><tr><td>KMDR</td><td>Kernel Matrix Dimension Reduction</td><td>A framework for construction of link similarity matrix from kernel matrix and feature transformation for DTI prediction [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>].</td></tr><tr><td>MGRNNM, DGRMC</td><td>Multi Graph Regularized Nuclear Norm Minimization</td><td>A computational method that adds multiple drug–graph and target–graph Laplacian regularization terms to the standard matrix completion framework to predict DTIs [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>].</td></tr><tr><td>WLNM</td><td>Weisfeiler-Lehman Neural Machine</td><td>An algorithm for extraction of the adjacency matrix that represents the interactions between potential drugs and targets [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>].</td></tr><tr><td>PDTPS</td><td>Predicting Drug Targets with Protein Sequence</td><td>A framework based on Relevance Vector Machine that integrates Bi-gram probabilities, PSSM and PCA [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>].</td></tr><tr><td></td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized Classifier</td><td>A regularized classifiers over the tensor product space of DT pairs for extracting informative and biologically meaningful features for DTI prediction [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>].</td></tr><tr><td>RBM</td><td>Restricted Boltzmann Machine</td><td>A two-layer undirected graphical model to represent a multidimensional DTI network and encode different types of DTIs [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>].</td></tr><tr><td>LRF-DTI</td><td>Lasso-based Random Forest method</td><td>A method of DTI prediction based on Lasso dimensionality reduction and random forest predictor [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>].</td></tr><tr><td>COSINE</td><td>COld Start INtEractions</td><td>A statistical dual-regularized, one-class collaborative filtering method [<span class="xrefLink" id="jumplink-ref217"></span><a href="javascript:;" reveal-id="ref217" data-open="ref217" class="link link-ref link-reveal xref-bibr">217</a>] framework and a corresponding computational method for multi-target virtual screening using one-class collaborative filtering technique that can employ either logistic or linear factorization [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>].</td></tr><tr><td>DMF</td><td>Deep Matrix Factorization</td><td>A deep learning approach in the context of recommendation systems to extract the non-linearity of latent variables [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>] (DMF was originally introduced in [<span class="xrefLink" id="jumplink-ref220"></span><a href="javascript:;" reveal-id="ref220" data-open="ref220" class="link link-ref link-reveal xref-bibr">220</a>] as a deep learning method in the context of recommendation systems to extract the non-linearity of latent variables).</td></tr><tr><td>CoDe-DTI</td><td>COllaborative DEep learning-based DTI predictor</td><td>A method using both PMF and a denoising autoencoder [<span class="xrefLink" id="jumplink-ref221"></span><a href="javascript:;" reveal-id="ref221" data-open="ref221" class="link link-ref link-reveal xref-bibr">221</a>].</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Abbreviations</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithms</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>escription<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>DT-Hybrid</td><td>Domain tuned-hybrid</td><td>An extended NBI technique that incorporates domain-based knowledge such as drug similarities and target similarities [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>] (also look [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref210"></span><a href="javascript:;" reveal-id="ref210" data-open="ref210" class="link link-ref link-reveal xref-bibr">210</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>, <span class="xrefLink" id="jumplink-ref211"></span><a href="javascript:;" reveal-id="ref211" data-open="ref211" class="link link-ref link-reveal xref-bibr">211</a>?? ] for extension of the capability of recommender systems).</td></tr><tr><td>KMDR</td><td>Kernel Matrix Dimension Reduction</td><td>A framework for construction of link similarity matrix from kernel matrix and feature transformation for DTI prediction [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>].</td></tr><tr><td>MGRNNM, DGRMC</td><td>Multi Graph Regularized Nuclear Norm Minimization</td><td>A computational method that adds multiple drug–graph and target–graph Laplacian regularization terms to the standard matrix completion framework to predict DTIs [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>].</td></tr><tr><td>WLNM</td><td>Weisfeiler-Lehman Neural Machine</td><td>An algorithm for extraction of the adjacency matrix that represents the interactions between potential drugs and targets [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>].</td></tr><tr><td>PDTPS</td><td>Predicting Drug Targets with Protein Sequence</td><td>A framework based on Relevance Vector Machine that integrates Bi-gram probabilities, PSSM and PCA [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>].</td></tr><tr><td></td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized Classifier</td><td>A regularized classifiers over the tensor product space of DT pairs for extracting informative and biologically meaningful features for DTI prediction [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>].</td></tr><tr><td>RBM</td><td>Restricted Boltzmann Machine</td><td>A two-layer undirected graphical model to represent a multidimensional DTI network and encode different types of DTIs [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>].</td></tr><tr><td>LRF-DTI</td><td>Lasso-based Random Forest method</td><td>A method of DTI prediction based on Lasso dimensionality reduction and random forest predictor [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>].</td></tr><tr><td>COSINE</td><td>COld Start INtEractions</td><td>A statistical dual-regularized, one-class collaborative filtering method [<span class="xrefLink" id="jumplink-ref217"></span><a href="javascript:;" reveal-id="ref217" data-open="ref217" class="link link-ref link-reveal xref-bibr">217</a>] framework and a corresponding computational method for multi-target virtual screening using one-class collaborative filtering technique that can employ either logistic or linear factorization [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>].</td></tr><tr><td>DMF</td><td>Deep Matrix Factorization</td><td>A deep learning approach in the context of recommendation systems to extract the non-linearity of latent variables [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>] (DMF was originally introduced in [<span class="xrefLink" id="jumplink-ref220"></span><a href="javascript:;" reveal-id="ref220" data-open="ref220" class="link link-ref link-reveal xref-bibr">220</a>] as a deep learning method in the context of recommendation systems to extract the non-linearity of latent variables).</td></tr><tr><td>CoDe-DTI</td><td>COllaborative DEep learning-based DTI predictor</td><td>A method using both PMF and a denoising autoencoder [<span class="xrefLink" id="jumplink-ref221"></span><a href="javascript:;" reveal-id="ref221" data-open="ref221" class="link link-ref link-reveal xref-bibr">221</a>].</td></tr></tbody></table></div></div></div> <h3 scrollto-destination=225532437 id="225532437" class="section-title js-splitscreen-section-title" data-legacy-id=sec2h>2.8 Software and packages</h3> <p class="chapter-para">Sakakibara <em>et al.</em> [<span class="xrefLink" id="jumplink-ref222"></span><a href="javascript:;" reveal-id="ref222" data-open="ref222" class="link link-ref link-reveal xref-bibr">222</a>] developed a web service called Comprehensive Predictor of Interactions between Chemical compounds And Target proteins based on their previous works [<span class="xrefLink" id="jumplink-ref127"></span><a href="javascript:;" reveal-id="ref127" data-open="ref127" class="link link-ref link-reveal xref-bibr">127</a>, <span class="xrefLink" id="jumplink-ref129"></span><a href="javascript:;" reveal-id="ref129" data-open="ref129" class="link link-ref link-reveal xref-bibr">129</a>] that uses SVM as the DTI predictor. This server seems to be no longer available.</p><p class="chapter-para">Cao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref223"></span><a href="javascript:;" reveal-id="ref223" data-open="ref223" class="link link-ref link-reveal xref-bibr">223</a>] developed a Python package called PyDPI based on Random Forest [<span class="xrefLink" id="jumplink-ref150"></span><a href="javascript:;" reveal-id="ref150" data-open="ref150" class="link link-ref link-reveal xref-bibr">150</a>] that integrates chemoinformatics, bioinformatics, proteochemometrics and chemogenomics for DTI prediction. The proposed framework involves the selection of molecular features and uses predefined dictionaries for classification. This package can be used to construct web-based servers and provides an interface for databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG), PubChem, Drugbank and Uniprot. The same group in the same year [<span class="xrefLink" id="jumplink-ref224"></span><a href="javascript:;" reveal-id="ref224" data-open="ref224" class="link link-ref link-reveal xref-bibr">224</a>] also developed a web-based server called PreDPI-Ki (which seems to be no longer available) based on a random forest predictor that takes binding affinities of DT pairs into account in order to better predict interactions.</p> <a id="225532440" scrollto-destination="225532440"></a> <div content-id="TB8" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB8" data-id="TB8"><span class="label title-label" id="label-97946">Table 8</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532440" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">DTI databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of int.</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Search fun.</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>ChEMBL</td><td>Dec 2018</td><td>12 482</td><td>1 879 206</td><td>15 504 603</td><td>✗</td><td>✓</td></tr><tr><td>ChemProt 3.0</td><td>Dec 2015</td><td>>20 000</td><td>>1 700 000</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>DGIdb 3.0</td><td>Nov 2017</td><td>41 100</td><td>9495</td><td>29 783</td><td>✗</td><td>✓</td></tr><tr><td>DrugBank</td><td>Apr 2019</td><td>5175</td><td>13 338</td><td>26 932</td><td>✗</td><td>✓</td></tr><tr><td>GtoPdb</td><td>Jun 2019</td><td>2926</td><td>9718</td><td>>50 000</td><td>✗</td><td>✓</td></tr><tr><td>IntAct</td><td>May 2019</td><td>102 508</td><td>10 849</td><td>593 007</td><td>✗</td><td>✓</td></tr><tr><td>KEGG</td><td>May 2019</td><td>-</td><td>-</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>LINCS</td><td>2016</td><td>1469</td><td>41 847</td><td>-</td><td>✗</td><td>✗</td></tr><tr><td>PROMISCUOUS</td><td>May 2011</td><td>6548</td><td>5258</td><td>23 702</td><td>✗</td><td>✓</td></tr><tr><td>STITCH</td><td>Jan 2016</td><td>>9 600 000</td><td>>430 000</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>SuperTarget</td><td>Oct 2011</td><td>>6000</td><td>>196 000</td><td>>330 000</td><td>✗</td><td>✓</td></tr><tr><td>TTD</td><td>Sep 2017</td><td>3101</td><td>34 019</td><td>-</td><td>✗</td><td>✓</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of int.</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Search fun.</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>ChEMBL</td><td>Dec 2018</td><td>12 482</td><td>1 879 206</td><td>15 504 603</td><td>✗</td><td>✓</td></tr><tr><td>ChemProt 3.0</td><td>Dec 2015</td><td>>20 000</td><td>>1 700 000</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>DGIdb 3.0</td><td>Nov 2017</td><td>41 100</td><td>9495</td><td>29 783</td><td>✗</td><td>✓</td></tr><tr><td>DrugBank</td><td>Apr 2019</td><td>5175</td><td>13 338</td><td>26 932</td><td>✗</td><td>✓</td></tr><tr><td>GtoPdb</td><td>Jun 2019</td><td>2926</td><td>9718</td><td>>50 000</td><td>✗</td><td>✓</td></tr><tr><td>IntAct</td><td>May 2019</td><td>102 508</td><td>10 849</td><td>593 007</td><td>✗</td><td>✓</td></tr><tr><td>KEGG</td><td>May 2019</td><td>-</td><td>-</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>LINCS</td><td>2016</td><td>1469</td><td>41 847</td><td>-</td><td>✗</td><td>✗</td></tr><tr><td>PROMISCUOUS</td><td>May 2011</td><td>6548</td><td>5258</td><td>23 702</td><td>✗</td><td>✓</td></tr><tr><td>STITCH</td><td>Jan 2016</td><td>>9 600 000</td><td>>430 000</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>SuperTarget</td><td>Oct 2011</td><td>>6000</td><td>>196 000</td><td>>330 000</td><td>✗</td><td>✓</td></tr><tr><td>TTD</td><td>Sep 2017</td><td>3101</td><td>34 019</td><td>-</td><td>✗</td><td>✓</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB8" data-id="TB8"><span class="label title-label" id="label-97946">Table 8</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532440" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">DTI databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of int.</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Search fun.</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>ChEMBL</td><td>Dec 2018</td><td>12 482</td><td>1 879 206</td><td>15 504 603</td><td>✗</td><td>✓</td></tr><tr><td>ChemProt 3.0</td><td>Dec 2015</td><td>>20 000</td><td>>1 700 000</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>DGIdb 3.0</td><td>Nov 2017</td><td>41 100</td><td>9495</td><td>29 783</td><td>✗</td><td>✓</td></tr><tr><td>DrugBank</td><td>Apr 2019</td><td>5175</td><td>13 338</td><td>26 932</td><td>✗</td><td>✓</td></tr><tr><td>GtoPdb</td><td>Jun 2019</td><td>2926</td><td>9718</td><td>>50 000</td><td>✗</td><td>✓</td></tr><tr><td>IntAct</td><td>May 2019</td><td>102 508</td><td>10 849</td><td>593 007</td><td>✗</td><td>✓</td></tr><tr><td>KEGG</td><td>May 2019</td><td>-</td><td>-</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>LINCS</td><td>2016</td><td>1469</td><td>41 847</td><td>-</td><td>✗</td><td>✗</td></tr><tr><td>PROMISCUOUS</td><td>May 2011</td><td>6548</td><td>5258</td><td>23 702</td><td>✗</td><td>✓</td></tr><tr><td>STITCH</td><td>Jan 2016</td><td>>9 600 000</td><td>>430 000</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>SuperTarget</td><td>Oct 2011</td><td>>6000</td><td>>196 000</td><td>>330 000</td><td>✗</td><td>✓</td></tr><tr><td>TTD</td><td>Sep 2017</td><td>3101</td><td>34 019</td><td>-</td><td>✗</td><td>✓</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of int.</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Search fun.</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>ChEMBL</td><td>Dec 2018</td><td>12 482</td><td>1 879 206</td><td>15 504 603</td><td>✗</td><td>✓</td></tr><tr><td>ChemProt 3.0</td><td>Dec 2015</td><td>>20 000</td><td>>1 700 000</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>DGIdb 3.0</td><td>Nov 2017</td><td>41 100</td><td>9495</td><td>29 783</td><td>✗</td><td>✓</td></tr><tr><td>DrugBank</td><td>Apr 2019</td><td>5175</td><td>13 338</td><td>26 932</td><td>✗</td><td>✓</td></tr><tr><td>GtoPdb</td><td>Jun 2019</td><td>2926</td><td>9718</td><td>>50 000</td><td>✗</td><td>✓</td></tr><tr><td>IntAct</td><td>May 2019</td><td>102 508</td><td>10 849</td><td>593 007</td><td>✗</td><td>✓</td></tr><tr><td>KEGG</td><td>May 2019</td><td>-</td><td>-</td><td>-</td><td>✗</td><td>✓</td></tr><tr><td>LINCS</td><td>2016</td><td>1469</td><td>41 847</td><td>-</td><td>✗</td><td>✗</td></tr><tr><td>PROMISCUOUS</td><td>May 2011</td><td>6548</td><td>5258</td><td>23 702</td><td>✗</td><td>✓</td></tr><tr><td>STITCH</td><td>Jan 2016</td><td>>9 600 000</td><td>>430 000</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>SuperTarget</td><td>Oct 2011</td><td>>6000</td><td>>196 000</td><td>>330 000</td><td>✗</td><td>✓</td></tr><tr><td>TTD</td><td>Sep 2017</td><td>3101</td><td>34 019</td><td>-</td><td>✗</td><td>✓</td></tr></tbody></table></div></div></div><p class="chapter-para">Xiao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref225"></span><a href="javascript:;" reveal-id="ref225" data-open="ref225" class="link link-ref link-reveal xref-bibr">225</a>] established a web server called iGPCR-drug, which is accessible at iGPCR-drug. Moreover, they developed a sequence-based classifier also called iGPCR-drug. In the predictor, the drug compound is formulated by a 2D fingerprint via a 256D vector, GPCRs by the pseudo amino acid composition [<span class="xrefLink" id="jumplink-ref226"></span><a href="javascript:;" reveal-id="ref226" data-open="ref226" class="link link-ref link-reveal xref-bibr">226</a>] generated with the gray model theory and the prediction engine is operated by the fuzzy K-nearest neighbor (KNN) classification method [<span class="xrefLink" id="jumplink-ref227"></span><a href="javascript:;" reveal-id="ref227" data-open="ref227" class="link link-ref link-reveal xref-bibr">227</a>]. The authors validated their method with the jackknife test [<span class="xrefLink" id="jumplink-ref228"></span><a href="javascript:;" reveal-id="ref228" data-open="ref228" class="link link-ref link-reveal xref-bibr">228</a>].</p><p class="chapter-para">Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref229"></span><a href="javascript:;" reveal-id="ref229" data-open="ref229" class="link link-ref link-reveal xref-bibr">229</a>] designed a web server called <a class="link link-uri openInAnotherWindow" href="http://DINIES" target="_blank">DINIES</a> (DTI network inference engine based on supervised analysis) for predicting DTI using various types of biological data such as chemical structures, protein domain and drug side effects (note that studies that primarily focused on side effect are excluded in this paper [<span class="xrefLink" id="jumplink-ref59 ref60 ref61 ref62"></span><a href="javascript:;" reveal-id="ref59 ref60 ref61 ref62" data-open="ref59 ref60 ref61 ref62" class="link link-ref link-reveal xref-bibr">59–62</a>]) and three supervised algorithms (BGL [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>, <span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>], BLM [<span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>] and pairwise kernel regression [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>]). This is due to the work by Scheiber <em>et al.</em> [<span class="xrefLink" id="jumplink-ref230"></span><a href="javascript:;" reveal-id="ref230" data-open="ref230" class="link link-ref link-reveal xref-bibr">230</a>] that enables the calculation of correlation between any drug compound and pharmacological effects in chemical space. While the training can be performed using KEGG DRUG database, the principle advantage of their web server is the flexibility of the input data, as long as it’s represented a similarity matrix or gene/drug profile.</p><p class="chapter-para">Seal <em>et al.</em> [<span class="xrefLink" id="jumplink-ref231"></span><a href="javascript:;" reveal-id="ref231" data-open="ref231" class="link link-ref link-reveal xref-bibr">231</a>] developed a standalone R and Shiny package called Netpredictor based on Random Walk with Restart (NRWRH) [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>, <span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>] and NBI [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>, <span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>] to predict any missing links between drugs, proteins and drug–proteins in any unipartite or bipartite. The main advantage of this package is the friendly user interface that is provided by package installation.</p><p class="chapter-para">Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref232"></span><a href="javascript:;" reveal-id="ref232" data-open="ref232" class="link link-ref link-reveal xref-bibr">232</a>] review, compare and reimplemented five state-of-the-art methods (BLM [<span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>], KronRLS-MKL [<span class="xrefLink" id="jumplink-ref158"></span><a href="javascript:;" reveal-id="ref158" data-open="ref158" class="link link-ref link-reveal xref-bibr">158</a>], DT-Hybrid [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>], the proposed method by Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>] and DNILMF [<span class="xrefLink" id="jumplink-ref233"></span><a href="javascript:;" reveal-id="ref233" data-open="ref233" class="link link-ref link-reveal xref-bibr">233</a>]) and published the source codes in R.</p> <h2 scrollto-destination=225532445 id="225532445" class="section-title js-splitscreen-section-title" data-legacy-id=sec3>3 Databases used in DTIpPrediction</h2> <p class="chapter-para">To support the above methods, many drug-related databases have been established. These databases contain different types of drug-related information and are critical resources for DTI predictions in silico. In this paper, we review all popular used databases associated with this topic. Based on the content of these databases, we classify them into four categories, DTI databases, drug-centered or target centered databases, drug–target binding affinity databases and supporting databases.</p> <h3 scrollto-destination=225532447 id="225532447" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a>3.1 DTI databases</h3> <p class="chapter-para">DTI databases are established for collecting DTIs and other related information. In this paper, we list 11 databases in this category. Within these databases, some are not directly proposed as ‘DTI’ databases, but the data contained can be used for DTI research. For example, KEGG is an extensive database that covers many types of biological data from genes/proteins to biological pathways and human diseases. In KEGG [<span class="xrefLink" id="jumplink-ref234"></span><a href="javascript:;" reveal-id="ref234" data-open="ref234" class="link link-ref link-reveal xref-bibr">234</a>], two subdatabases, KEGGDRUG [<span class="xrefLink" id="jumplink-ref235"></span><a href="javascript:;" reveal-id="ref235" data-open="ref235" class="link link-ref link-reveal xref-bibr">235</a>] and KEGGBRITE [<span class="xrefLink" id="jumplink-ref236"></span><a href="javascript:;" reveal-id="ref236" data-open="ref236" class="link link-ref link-reveal xref-bibr">236</a>] contain data that can be used for DTI predictions. ChEMBL [<span class="xrefLink" id="jumplink-ref237 ref238 ref239"></span><a href="javascript:;" reveal-id="ref237 ref238 ref239" data-open="ref237 ref238 ref239" class="link link-ref link-reveal xref-bibr">237–239</a>] is also not specifically a ‘drug-target’ database and it was established based on collecting bioactive compounds. However, combined with targets and other related biological information, this database can also be used in drug-target repositioning and repurposing. Similar to ChEMBL [<span class="xrefLink" id="jumplink-ref237 ref238 ref239"></span><a href="javascript:;" reveal-id="ref237 ref238 ref239" data-open="ref237 ref238 ref239" class="link link-ref link-reveal xref-bibr">237–239</a>], IntAct [<span class="xrefLink" id="jumplink-ref240"></span><a href="javascript:;" reveal-id="ref240" data-open="ref240" class="link link-ref link-reveal xref-bibr">240</a>] is a database that contains molecular interactions and can be used for drug research. LINCS is different from the aforementioned two databases. This data portal contains biochemistry data that aims to understand changes in gene expression and cellular processes that are caused by different perturbing agents. Many of the perturbing agents used in LINCS are drugs, so this is also a great data source for DTI research. Other databases included in this group are SuperTarget [<span class="xrefLink" id="jumplink-ref241"></span><a href="javascript:;" reveal-id="ref241" data-open="ref241" class="link link-ref link-reveal xref-bibr">241</a>], Guide to PHARMACOLOGY (GtoPdb) [<span class="xrefLink" id="jumplink-ref240"></span><a href="javascript:;" reveal-id="ref240" data-open="ref240" class="link link-ref link-reveal xref-bibr">240</a>], DrugBank [<span class="xrefLink" id="jumplink-ref242 ref243 ref244 ref245 ref246"></span><a href="javascript:;" reveal-id="ref242 ref243 ref244 ref245 ref246" data-open="ref242 ref243 ref244 ref245 ref246" class="link link-ref link-reveal xref-bibr">242–246</a>], Therapeutic Targets Database (TTD) [<span class="xrefLink" id="jumplink-ref247"></span><a href="javascript:;" reveal-id="ref247" data-open="ref247" class="link link-ref link-reveal xref-bibr">247</a>], STITCH [<span class="xrefLink" id="jumplink-ref248 ref249 ref250 ref251 ref252"></span><a href="javascript:;" reveal-id="ref248 ref249 ref250 ref251 ref252" data-open="ref248 ref249 ref250 ref251 ref252" class="link link-ref link-reveal xref-bibr">248–252</a>], ChemProt 3.0 [<span class="xrefLink" id="jumplink-ref253"></span><a href="javascript:;" reveal-id="ref253" data-open="ref253" class="link link-ref link-reveal xref-bibr">253</a>] and DGIdb 3.0 [<span class="xrefLink" id="jumplink-ref254"></span><a href="javascript:;" reveal-id="ref254" data-open="ref254" class="link link-ref link-reveal xref-bibr">254</a>]. The general information for these databases is summarized in Table <span class="xrefLink" id="jumplink-TB8"></span><a href="javascript:;" reveal-id="TB8" data-open="TB8" class="link link-reveal link-table xref-fig">8</a>.</p> <h4 scrollto-destination=225532449 id="225532449" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a1>3.1.1 ChEMBL</h4> <p class="chapter-para">The data stored in the ChEMBL database [<span class="xrefLink" id="jumplink-ref237 ref238 ref239"></span><a href="javascript:;" reveal-id="ref237 ref238 ref239" data-open="ref237 ref238 ref239" class="link link-ref link-reveal xref-bibr">237–239</a>] were manually extracted from published literatures. This database was published by European Molecular Biology Laboratory (EMBL)-European Bioinformatics Institute in 2002. Since the latest update in 2018, this database contains more than 1.9 million chemical compounds. Within these compounds, over 10 thousand drugs and more than 12 thousand targets are included in ChEMBL.</p> <h4 scrollto-destination=225532451 id="225532451" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a2>3.1.2 ChemProt 3.0</h4> <p class="chapter-para">ChemProt [<span class="xrefLink" id="jumplink-ref253"></span><a href="javascript:;" reveal-id="ref253" data-open="ref253" class="link link-ref link-reveal xref-bibr">253</a>, <span class="xrefLink" id="jumplink-ref255"></span><a href="javascript:;" reveal-id="ref255" data-open="ref255" class="link link-ref link-reveal xref-bibr">255</a>, <span class="xrefLink" id="jumplink-ref256"></span><a href="javascript:;" reveal-id="ref256" data-open="ref256" class="link link-ref link-reveal xref-bibr">256</a>] was proposed as a disease chemical biology database that integrated data from multiple chemical–protein annotation databases and disease-associated PPI. The first release of ChemProt was in 2011, which collected data from eight public databases, i.e. ChEMBL [<span class="xrefLink" id="jumplink-ref238"></span><a href="javascript:;" reveal-id="ref238" data-open="ref238" class="link link-ref link-reveal xref-bibr">238</a>], BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>], PDSP Ki database [<span class="xrefLink" id="jumplink-ref258"></span><a href="javascript:;" reveal-id="ref258" data-open="ref258" class="link link-ref link-reveal xref-bibr">258</a>], DrugBank [<span class="xrefLink" id="jumplink-ref244"></span><a href="javascript:;" reveal-id="ref244" data-open="ref244" class="link link-ref link-reveal xref-bibr">244</a>], PharmGKB [<span class="xrefLink" id="jumplink-ref259"></span><a href="javascript:;" reveal-id="ref259" data-open="ref259" class="link link-ref link-reveal xref-bibr">259</a>], PubChem bioassay [<span class="xrefLink" id="jumplink-ref260"></span><a href="javascript:;" reveal-id="ref260" data-open="ref260" class="link link-ref link-reveal xref-bibr">260</a>], CTD [<span class="xrefLink" id="jumplink-ref261"></span><a href="javascript:;" reveal-id="ref261" data-open="ref261" class="link link-ref link-reveal xref-bibr">261</a>] and STITCH [<span class="xrefLink" id="jumplink-ref248"></span><a href="javascript:;" reveal-id="ref248" data-open="ref248" class="link link-ref link-reveal xref-bibr">248</a>] and two commercial databases, WOMBAT and WOMBAT-PK [<span class="xrefLink" id="jumplink-ref262"></span><a href="javascript:;" reveal-id="ref262" data-open="ref262" class="link link-ref link-reveal xref-bibr">262</a>]. The second update of ChemProt was in 2012 integrated therapeutic effects and adverse drug reactions into the 2.0 version. The latest update (version 3.0) was released in 2015. The third version updated the disease chemical biology data. In addition, several computational methods, such as network biology based enrichment analysis, were also incorporated.</p> <h4 scrollto-destination=225532453 id="225532453" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a3>3.1.3 DGIdb 3.0</h4> <p class="chapter-para">The first release (in 2013) of DGIdb integrated 13 data sources that cover information in disease-related human genes, drugs, drug interactions and potential druggability [<span class="xrefLink" id="jumplink-ref263"></span><a href="javascript:;" reveal-id="ref263" data-open="ref263" class="link link-ref link-reveal xref-bibr">263</a>, <span class="xrefLink" id="jumplink-ref264"></span><a href="javascript:;" reveal-id="ref264" data-open="ref264" class="link link-ref link-reveal xref-bibr">264</a>]. The latest update of DGIdb was in 2017 and in total 30 data sources are included in the 3.0 version [<span class="xrefLink" id="jumplink-ref254"></span><a href="javascript:;" reveal-id="ref254" data-open="ref254" class="link link-ref link-reveal xref-bibr">254</a>]. Six new data sources were added and nine of the previous data sources were updated.</p> <h4 scrollto-destination=225532455 id="225532455" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a4>3.1.4 DrugBank</h4> <p class="chapter-para">DrugBank [<span class="xrefLink" id="jumplink-ref242 ref243 ref244 ref245 ref246"></span><a href="javascript:;" reveal-id="ref242 ref243 ref244 ref245 ref246" data-open="ref242 ref243 ref244 ref245 ref246" class="link link-ref link-reveal xref-bibr">242–246</a>] is one of the most popular databasesand has been widely used as a drug reference resource. This database was first released in 2006. As a database both in bioinformatics and cheminformatics, DrugBank contains detailed drug data with comprehensive drug target information. The DTI relationships in DrugBank were originally collected from textbooks, published articles and other electronic databases. All data can be freely downloaded from DrugBank.</p> <h4 scrollto-destination=225532457 id="225532457" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a5>3.1.5 GtoPdb</h4> <p class="chapter-para">This database was established by the International Union of Basic and Clinical Pharmacology/British Pharmacological Society. The GtoPdb [<span class="xrefLink" id="jumplink-ref240"></span><a href="javascript:;" reveal-id="ref240" data-open="ref240" class="link link-ref link-reveal xref-bibr">240</a>] contains the ligand–activity–target relationships data that were collected from pharmacological and medicine chemistry literature.</p> <h4 scrollto-destination=225532459 id="225532459" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a6>3.1.6 IntAct</h4> <p class="chapter-para">IntAct [<span class="xrefLink" id="jumplink-ref265"></span><a href="javascript:;" reveal-id="ref265" data-open="ref265" class="link link-ref link-reveal xref-bibr">265</a>] is an open source database of molecular interactions populated by data from literature and other data sources. In total, 11 molecular interaction databases (including IntAct) were incorporated into IntAct including AgBase [<span class="xrefLink" id="jumplink-ref266 ref267 ref268 ref269"></span><a href="javascript:;" reveal-id="ref266 ref267 ref268 ref269" data-open="ref266 ref267 ref268 ref269" class="link link-ref link-reveal xref-bibr">266–269</a>], MINT [<span class="xrefLink" id="jumplink-ref270 ref271 ref272 ref273"></span><a href="javascript:;" reveal-id="ref270 ref271 ref272 ref273" data-open="ref270 ref271 ref272 ref273" class="link link-ref link-reveal xref-bibr">270–273</a>], UniProt [<span class="xrefLink" id="jumplink-ref274"></span><a href="javascript:;" reveal-id="ref274" data-open="ref274" class="link link-ref link-reveal xref-bibr">274</a>][41], I2D [<span class="xrefLink" id="jumplink-ref275"></span><a href="javascript:;" reveal-id="ref275" data-open="ref275" class="link link-ref link-reveal xref-bibr">275</a>], MBINFO, MatrixDB [<span class="xrefLink" id="jumplink-ref276"></span><a href="javascript:;" reveal-id="ref276" data-open="ref276" class="link link-ref link-reveal xref-bibr">276</a>], Molecular Connections, InnateDN [<span class="xrefLink" id="jumplink-ref277"></span><a href="javascript:;" reveal-id="ref277" data-open="ref277" class="link link-ref link-reveal xref-bibr">277</a>], IMEx [<span class="xrefLink" id="jumplink-ref278"></span><a href="javascript:;" reveal-id="ref278" data-open="ref278" class="link link-ref link-reveal xref-bibr">278</a>] and <a class="link link-uri openInAnotherWindow" href="http://GOA" target="_blank">GOA</a>.</p> <h4 scrollto-destination=225532461 id="225532461" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a7>3.1.7 KEGG</h4> <p class="chapter-para">KEGG is a comprehensive database that provides many types of knowledge about genes and genomes [<span class="xrefLink" id="jumplink-ref234"></span><a href="javascript:;" reveal-id="ref234" data-open="ref234" class="link link-ref link-reveal xref-bibr">234</a>, <span class="xrefLink" id="jumplink-ref235"></span><a href="javascript:;" reveal-id="ref235" data-open="ref235" class="link link-ref link-reveal xref-bibr">235</a>]. The whole database can be summarized in four major categories. The first one is systems information, contains three databases: KEGG PATHWAY, KEGG BRITE, and KEGG MODULE. The second category contain genomic information. In this group, four databases are included: KEGG ORTHOLOGY, KEGG GENOME, KEGG GENES and KEGG SSDB. The third category holds the chemical information. Five databases are in this category: KEGG COMPOUND, KEGG GLYCAN, KEGG REACTION, KEGG RCLASS and KEGG ENZYME. The last category is health information that covers four databases: KEGG DISEASE, KEGGDRUG, KEGG DGROUP and KEGG ENVIRON. The KEGG DGROUP database contains information regarding drug interaction networks including DTIs, drug metabolism and indirect interactions with enzymes and target genes.</p> <h4 scrollto-destination=225532463 id="225532463" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a8>3.1.8 LINCS</h4> <p class="chapter-para">The LINCS program aims to establish a network-based landscape to describe how different perturbing agents influence cellular processes. In total, there are 398 datasets collected in the LINCS database including fluorescence imaging, ELISA and ATAC-seq data, etc. The majority datasets (177 datasets) in LINCS are KINOMEscan kinase-small molecule binding assays. This assay is used to measure binding interactions between test compounds.</p> <h4 scrollto-destination=225532465 id="225532465" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a9>3.1.9 PROMISCUOUS</h4> <p class="chapter-para">PROMISCUOUS was established in 2011 and proposed as a database for network-based drug repositioning. This database contains three different types of data: drugs, proteins and side effects. The protein data are extracted from UniProt and incorporated with the 3D structure information from Protein Data Bank (PDB). Drugs and side effects are extracted and incorporated from SuperDrug and SIDER, respectively. In addition to DTIs and drug side effects linkages, PROMISCUOUS also includes data on drug–drug similarities and PPI.</p> <h4 scrollto-destination=225532467 id="225532467" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a10>3.1.10 STITCH</h4> <p class="chapter-para">STITCH [<span class="xrefLink" id="jumplink-ref248 ref249 ref250 ref251 ref252"></span><a href="javascript:;" reveal-id="ref248 ref249 ref250 ref251 ref252" data-open="ref248 ref249 ref250 ref251 ref252" class="link link-ref link-reveal xref-bibr">248–252</a>] is a database that stores information for interactions between proteins and small molecules. The interaction data are collected from predicted results, other databases (e.g. PubChem [<span class="xrefLink" id="jumplink-ref279"></span><a href="javascript:;" reveal-id="ref279" data-open="ref279" class="link link-ref link-reveal xref-bibr">279</a>]), and literature. The first release of STITCH was in 2008.</p> <h4 scrollto-destination=225532469 id="225532469" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a11>3.1.11 SuperTarget</h4> <p class="chapter-para">SuperTarget [<span class="xrefLink" id="jumplink-ref241"></span><a href="javascript:;" reveal-id="ref241" data-open="ref241" class="link link-ref link-reveal xref-bibr">241</a>] is a database that covers DTI information with drug metabolism, pathways and Gene Ontology (GO) terms. Medical indications and adverse drug effects are also included in this database. The DTIs information in this database were extracted starting with text mining from 15 million public literature listed in PubMed. Also, potential drug–target relations were also extracted from Medline. Furthermore, the relationships of DTIs from other databases (i.e. DrugBank [<span class="xrefLink" id="jumplink-ref244"></span><a href="javascript:;" reveal-id="ref244" data-open="ref244" class="link link-ref link-reveal xref-bibr">244</a>], KEGG [<span class="xrefLink" id="jumplink-ref234"></span><a href="javascript:;" reveal-id="ref234" data-open="ref234" class="link link-ref link-reveal xref-bibr">234</a>], PDB [<span class="xrefLink" id="jumplink-ref280"></span><a href="javascript:;" reveal-id="ref280" data-open="ref280" class="link link-ref link-reveal xref-bibr">280</a>], SuperLigands [<span class="xrefLink" id="jumplink-ref281"></span><a href="javascript:;" reveal-id="ref281" data-open="ref281" class="link link-ref link-reveal xref-bibr">281</a>] and TTD [<span class="xrefLink" id="jumplink-ref282"></span><a href="javascript:;" reveal-id="ref282" data-open="ref282" class="link link-ref link-reveal xref-bibr">282</a>]) were also used to obtain any missed DTIs that were not included from the previous two strategies.</p> <h4 scrollto-destination=225532471 id="225532471" class="section-title js-splitscreen-section-title" data-legacy-id=sec3a12>3.1.12 TTD</h4> <p class="chapter-para">TTD provides therapeutic proteins, nucleic acid targets and corresponding drug information [<span class="xrefLink" id="jumplink-ref247"></span><a href="javascript:;" reveal-id="ref247" data-open="ref247" class="link link-ref link-reveal xref-bibr">247</a>]. This database was first described in 2002. The data in TTD was mainly collected from literature. Other databases that contains DTIs information (e.g. KEGG) were also cross-linked to TTD.</p> <h3 scrollto-destination=225532473 id="225532473" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n>3.2 Drug-centered or target-centered databases</h3> <p class="chapter-para">In this category, six databases are included. They are BRENDA [<span class="xrefLink" id="jumplink-ref283"></span><a href="javascript:;" reveal-id="ref283" data-open="ref283" class="link link-ref link-reveal xref-bibr">283</a>], PubChem [<span class="xrefLink" id="jumplink-ref279"></span><a href="javascript:;" reveal-id="ref279" data-open="ref279" class="link link-ref link-reveal xref-bibr">279</a>], SuperDRUG2 [<span class="xrefLink" id="jumplink-ref284"></span><a href="javascript:;" reveal-id="ref284" data-open="ref284" class="link link-ref link-reveal xref-bibr">284</a>], DrugCentral [<span class="xrefLink" id="jumplink-ref285"></span><a href="javascript:;" reveal-id="ref285" data-open="ref285" class="link link-ref link-reveal xref-bibr">285</a>, <span class="xrefLink" id="jumplink-ref286"></span><a href="javascript:;" reveal-id="ref286" data-open="ref286" class="link link-ref link-reveal xref-bibr">286</a>], PDID [<span class="xrefLink" id="jumplink-ref287"></span><a href="javascript:;" reveal-id="ref287" data-open="ref287" class="link link-ref link-reveal xref-bibr">287</a>], Pharos [<span class="xrefLink" id="jumplink-ref288"></span><a href="javascript:;" reveal-id="ref288" data-open="ref288" class="link link-ref link-reveal xref-bibr">288</a>] and ECOdrug [<span class="xrefLink" id="jumplink-ref289"></span><a href="javascript:;" reveal-id="ref289" data-open="ref289" class="link link-ref link-reveal xref-bibr">289</a>].</p><p class="chapter-para">Among these databases, SuperDRUG2 and DrugCentral are proposed as ‘drug-centered’ databases. Since PubChem is a database established on collecting millions of chemical compounds, in this paper, we also list this one as a ‘drug-centered’ database. PDID and Pharos are classified as ‘target-centered’ databases. We also included BRENDA as a ‘target database’. The huge amount of enzymes and related ligands stored in BRENDA can be used as targets in DTI research. In addition, we also list ECOdrug here as a target-centered database. Different from the aforementioned ones, this database contains target information in non-human model species. Relative information can be found in Table <span class="xrefLink" id="jumplink-TB9"></span><a href="javascript:;" reveal-id="TB9" data-open="TB9" class="link link-reveal link-table xref-fig">9</a>.</p> <a id="225532476" scrollto-destination="225532476"></a> <div content-id="TB9" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB9" data-id="TB9"><span class="label title-label" id="label-97946">Table 9</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532476" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">Drug-centered or Target-centered databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Type</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/Compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BRENDA</td><td>Jan 2019</td><td>Target centered</td><td>>84 000</td><td>>205 000</td><td>✗</td></tr><tr><td>DrugCentral</td><td>Apr,2019</td><td>Drug centered</td><td>-</td><td>4543</td><td>✗</td></tr><tr><td>ECOdrug</td><td>Oct 2017</td><td>Target centered</td><td>-</td><td>-</td><td>✗</td></tr><tr><td>PDID</td><td>Apr 2015</td><td>Target centered</td><td>3746</td><td>51</td><td>✓</td></tr><tr><td>Pharos</td><td>Nov 2018</td><td>Target centered</td><td>20 244</td><td>130 166</td><td>✗</td></tr><tr><td>PubChem</td><td>Mar 2019</td><td>Drug centered</td><td>79 622</td><td>96 157 016</td><td>✗</td></tr><tr><td>SuperDRUG2</td><td>Mar 2018</td><td>Drug centered</td><td>4456</td><td>4605</td><td>✓</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Type</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/Compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BRENDA</td><td>Jan 2019</td><td>Target centered</td><td>>84 000</td><td>>205 000</td><td>✗</td></tr><tr><td>DrugCentral</td><td>Apr,2019</td><td>Drug centered</td><td>-</td><td>4543</td><td>✗</td></tr><tr><td>ECOdrug</td><td>Oct 2017</td><td>Target centered</td><td>-</td><td>-</td><td>✗</td></tr><tr><td>PDID</td><td>Apr 2015</td><td>Target centered</td><td>3746</td><td>51</td><td>✓</td></tr><tr><td>Pharos</td><td>Nov 2018</td><td>Target centered</td><td>20 244</td><td>130 166</td><td>✗</td></tr><tr><td>PubChem</td><td>Mar 2019</td><td>Drug centered</td><td>79 622</td><td>96 157 016</td><td>✗</td></tr><tr><td>SuperDRUG2</td><td>Mar 2018</td><td>Drug centered</td><td>4456</td><td>4605</td><td>✓</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB9" data-id="TB9"><span class="label title-label" id="label-97946">Table 9</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532476" aria-describedby="label-97946"> Open in new tab </a></div><div class="caption caption-id-" id="caption-97946"><p class="chapter-para">Drug-centered or Target-centered databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-97946" aria-describedby="
 caption-97946"><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Type</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/Compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BRENDA</td><td>Jan 2019</td><td>Target centered</td><td>>84 000</td><td>>205 000</td><td>✗</td></tr><tr><td>DrugCentral</td><td>Apr,2019</td><td>Drug centered</td><td>-</td><td>4543</td><td>✗</td></tr><tr><td>ECOdrug</td><td>Oct 2017</td><td>Target centered</td><td>-</td><td>-</td><td>✗</td></tr><tr><td>PDID</td><td>Apr 2015</td><td>Target centered</td><td>3746</td><td>51</td><td>✓</td></tr><tr><td>Pharos</td><td>Nov 2018</td><td>Target centered</td><td>20 244</td><td>130 166</td><td>✗</td></tr><tr><td>PubChem</td><td>Mar 2019</td><td>Drug centered</td><td>79 622</td><td>96 157 016</td><td>✗</td></tr><tr><td>SuperDRUG2</td><td>Mar 2018</td><td>Drug centered</td><td>4456</td><td>4605</td><td>✓</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Type</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/Compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Predicted DTIs</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BRENDA</td><td>Jan 2019</td><td>Target centered</td><td>>84 000</td><td>>205 000</td><td>✗</td></tr><tr><td>DrugCentral</td><td>Apr,2019</td><td>Drug centered</td><td>-</td><td>4543</td><td>✗</td></tr><tr><td>ECOdrug</td><td>Oct 2017</td><td>Target centered</td><td>-</td><td>-</td><td>✗</td></tr><tr><td>PDID</td><td>Apr 2015</td><td>Target centered</td><td>3746</td><td>51</td><td>✓</td></tr><tr><td>Pharos</td><td>Nov 2018</td><td>Target centered</td><td>20 244</td><td>130 166</td><td>✗</td></tr><tr><td>PubChem</td><td>Mar 2019</td><td>Drug centered</td><td>79 622</td><td>96 157 016</td><td>✗</td></tr><tr><td>SuperDRUG2</td><td>Mar 2018</td><td>Drug centered</td><td>4456</td><td>4605</td><td>✓</td></tr></tbody></table></div></div></div> <h4 scrollto-destination=225532477 id="225532477" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n1>3.2.1 BRENDA</h4> <p class="chapter-para">BRENDA [<span class="xrefLink" id="jumplink-ref283"></span><a href="javascript:;" reveal-id="ref283" data-open="ref283" class="link link-ref link-reveal xref-bibr">283</a>, <span class="xrefLink" id="jumplink-ref290"></span><a href="javascript:;" reveal-id="ref290" data-open="ref290" class="link link-ref link-reveal xref-bibr">290</a>] is a comprehensive enzyme database that was first established in 1987. This database contains ˜84 000 enzymes and their corresponding enzyme–ligand related information. All data collected in this database was manually evaluated and extracted from ˜140 000 literature references based on the Enzyme Commission (EC) classification system of the International Union of Biochemistry and Molecular Biology. All compounds related to enzyme catalyzed reactions are labeled as ‘ligands’ in BRENDA, such as substrates, products, activators, inhibitors and cofactors. In total, about 205 000 enzyme ligands were collected and stored in the associated ligand database. Users can search the ligand database through the search box on the home page. BRENDA also provides download functionality for users to download all BRENDA data.</p> <h4 scrollto-destination=225532479 id="225532479" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n2>3.2.2 DrugCentral</h4> <p class="chapter-para">DrugCentral is a comprehensive database that focuses on drug collection [<span class="xrefLink" id="jumplink-ref285"></span><a href="javascript:;" reveal-id="ref285" data-open="ref285" class="link link-ref link-reveal xref-bibr">285</a>, <span class="xrefLink" id="jumplink-ref286"></span><a href="javascript:;" reveal-id="ref286" data-open="ref286" class="link link-ref link-reveal xref-bibr">286</a>]. This database was released in 2016 and contains approved active pharmaceutical ingredients (drugs) from FDA and other regulatory agencies. For each drug, structure information, bioactivity and regulatory records, as well as pharmacologic actions and indications were incorporated. In this database, all drugs are simply classified into three categories, small molecule active ingredients, biological active ingredients and others.</p> <h4 scrollto-destination=225532481 id="225532481" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n3>3.2.3 ECOdrug</h4> <p class="chapter-para">In drug discovery research, non-human model species are important in that they are used for drug testing. ECOdrug [<span class="xrefLink" id="jumplink-ref289"></span><a href="javascript:;" reveal-id="ref289" data-open="ref289" class="link link-ref link-reveal xref-bibr">289</a>] is a database that contains DTI data for 640 eukaryotic species. The data stored in ECOdrug can help researchers investigate the conservation of human drug targets across species. The drug information and drug targets are from previous research [<span class="xrefLink" id="jumplink-ref291"></span><a href="javascript:;" reveal-id="ref291" data-open="ref291" class="link link-ref link-reveal xref-bibr">291</a>] and DrugBank [<span class="xrefLink" id="jumplink-ref244"></span><a href="javascript:;" reveal-id="ref244" data-open="ref244" class="link link-ref link-reveal xref-bibr">244</a>].</p> <h4 scrollto-destination=225532483 id="225532483" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n4>3.2.4 PDID</h4> <p class="chapter-para">PDID [<span class="xrefLink" id="jumplink-ref287"></span><a href="javascript:;" reveal-id="ref287" data-open="ref287" class="link link-ref link-reveal xref-bibr">287</a>] was released in 2014 and covers all known protein–drug interactions and predicted protein–drug interactions for the entire structural human proteome. The known interactions were extracted from DrugBank [<span class="xrefLink" id="jumplink-ref244"></span><a href="javascript:;" reveal-id="ref244" data-open="ref244" class="link link-ref link-reveal xref-bibr">244</a>], BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>] and PDB [<span class="xrefLink" id="jumplink-ref280"></span><a href="javascript:;" reveal-id="ref280" data-open="ref280" class="link link-ref link-reveal xref-bibr">280</a>]. The predictions were made by using three different softwares (i.e. ILbind [<span class="xrefLink" id="jumplink-ref292"></span><a href="javascript:;" reveal-id="ref292" data-open="ref292" class="link link-ref link-reveal xref-bibr">292</a>], SMAP [<span class="xrefLink" id="jumplink-ref45"></span><a href="javascript:;" reveal-id="ref45" data-open="ref45" class="link link-ref link-reveal xref-bibr">45</a>] and eFindSite [<span class="xrefLink" id="jumplink-ref293"></span><a href="javascript:;" reveal-id="ref293" data-open="ref293" class="link link-ref link-reveal xref-bibr">293</a>, <span class="xrefLink" id="jumplink-ref294"></span><a href="javascript:;" reveal-id="ref294" data-open="ref294" class="link link-ref link-reveal xref-bibr">294</a>]).</p> <h4 scrollto-destination=225532485 id="225532485" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n5>3.2.5 Pharos</h4> <p class="chapter-para">Pharos [<span class="xrefLink" id="jumplink-ref288"></span><a href="javascript:;" reveal-id="ref288" data-open="ref288" class="link link-ref link-reveal xref-bibr">288</a>] is a platform that was established for presenting the data in the Target Central Resource Database (TCRD). TCRD is a comprehensive database that was initially developed for discovering new druggable proteins.</p><p class="chapter-para">The data stored in TCRD came from many different sources. It includes biomedical literature, expression data, disease and phenotype association data, bioactivity data, DTI data and databases from Harmonizome [<span class="xrefLink" id="jumplink-ref295"></span><a href="javascript:;" reveal-id="ref295" data-open="ref295" class="link link-ref link-reveal xref-bibr">295</a>].</p> <h4 scrollto-destination=225532488 id="225532488" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n6>3.2.6 PubChem</h4> <p class="chapter-para">PubChem [<span class="xrefLink" id="jumplink-ref279"></span><a href="javascript:;" reveal-id="ref279" data-open="ref279" class="link link-ref link-reveal xref-bibr">279</a>, <span class="xrefLink" id="jumplink-ref296"></span><a href="javascript:;" reveal-id="ref296" data-open="ref296" class="link link-ref link-reveal xref-bibr">296</a>] stores the information of chemical substances and corresponding biological actives. This database consists of three sub-databases: Substance, Compound and BioAssay. Substance is the primary repository to store chemical information provided from individual data contributors. The Compound database contains the unique chemical structures extracted from the Substance database. All biological related data of these chemical substance data are saved in the BioAssay database.</p> <h4 scrollto-destination=225532490 id="225532490" class="section-title js-splitscreen-section-title" data-legacy-id=sec3n7>3.2.7 SuperDRUG2</h4> <p class="chapter-para">SuperDRUG2 [<span class="xrefLink" id="jumplink-ref284"></span><a href="javascript:;" reveal-id="ref284" data-open="ref284" class="link link-ref link-reveal xref-bibr">284</a>] is proposed as a one-stop data source that offers all crucial features of approved and marketed drugs. The drug items in SuperDRUG2 are classified into two categories: small molecules and biological/other drugs. Several public resources like US FDA, CFDA and EMA, etc. were used for drug collections. Drug target information in SuperDRUG2 was extracted from DrugBank [<span class="xrefLink" id="jumplink-ref244"></span><a href="javascript:;" reveal-id="ref244" data-open="ref244" class="link link-ref link-reveal xref-bibr">244</a>], TTD [<span class="xrefLink" id="jumplink-ref247"></span><a href="javascript:;" reveal-id="ref247" data-open="ref247" class="link link-ref link-reveal xref-bibr">247</a>] and ChEMBL [<span class="xrefLink" id="jumplink-ref238"></span><a href="javascript:;" reveal-id="ref238" data-open="ref238" class="link link-ref link-reveal xref-bibr">238</a>]. Besides these drugs and targets information, SuperDRUG2 also provides 2D and 3D structure information of small molecule drugs, drug side effects, drug–drug interactions and drug pharmacokinetic parameters.</p> <h3 scrollto-destination=225532492 id="225532492" class="section-title js-splitscreen-section-title" data-legacy-id=sec3v>3.3 Binding affinity databases</h3> <p class="chapter-para">In this category, BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>, <span class="xrefLink" id="jumplink-ref297 ref298 ref299"></span><a href="javascript:;" reveal-id="ref297 ref298 ref299" data-open="ref297 ref298 ref299" class="link link-ref link-reveal xref-bibr">297–299</a>], PDBBind [<span class="xrefLink" id="jumplink-ref300"></span><a href="javascript:;" reveal-id="ref300" data-open="ref300" class="link link-ref link-reveal xref-bibr">300</a>] and PDSP Ki [<span class="xrefLink" id="jumplink-ref301"></span><a href="javascript:;" reveal-id="ref301" data-open="ref301" class="link link-ref link-reveal xref-bibr">301</a>] are included. All of them contain the data on chemical-protein binding affinities. BindingDB is mainly focused on collection of binding affinity data between drugs (drug-like molecules) and target proteins. PDBbind is established based on binding affinity measurements of biomolecular complexes from PDB. PDSP Ki is similar to BindingDB, which also contains a large number of binding affinity data on DTIs. Table <span class="xrefLink" id="jumplink-TB10"></span><a href="javascript:;" reveal-id="TB10" data-open="TB10" class="link link-reveal link-table xref-fig">10</a> shows the relative information of these three databases.</p> <a id="225532494" scrollto-destination="225532494"></a> <div content-id="TB10" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB10" data-id="TB10"><span class="label title-label" id="label-81584">Table 10</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532494" aria-describedby="label-81584"> Open in new tab </a></div><div class="caption caption-id-" id="caption-81584"><p class="chapter-para">Binding affinity databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-81584" aria-describedby="
 caption-81584"><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of DTI</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of TTI</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BindingDB</td><td>May 2019</td><td>7269</td><td>733198</td><td>1651120</td><td>-</td></tr><tr><td>PDBBind</td><td>Jan 2018</td><td>-</td><td>-</td><td>16276</td><td>3312</td></tr><tr><td>PDSP Ki</td><td>2019</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of DTI</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of TTI</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BindingDB</td><td>May 2019</td><td>7269</td><td>733198</td><td>1651120</td><td>-</td></tr><tr><td>PDBBind</td><td>Jan 2018</td><td>-</td><td>-</td><td>16276</td><td>3312</td></tr><tr><td>PDSP Ki</td><td>2019</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB10" data-id="TB10"><span class="label title-label" id="label-81584">Table 10</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532494" aria-describedby="label-81584"> Open in new tab </a></div><div class="caption caption-id-" id="caption-81584"><p class="chapter-para">Binding affinity databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-81584" aria-describedby="
 caption-81584"><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of DTI</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of TTI</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BindingDB</td><td>May 2019</td><td>7269</td><td>733198</td><td>1651120</td><td>-</td></tr><tr><td>PDBBind</td><td>Jan 2018</td><td>-</td><td>-</td><td>16276</td><td>3312</td></tr><tr><td>PDSP Ki</td><td>2019</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Latest updates</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of targets</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of drugs/compounds</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of DTI</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>No. of TTI</strong><span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>BindingDB</td><td>May 2019</td><td>7269</td><td>733198</td><td>1651120</td><td>-</td></tr><tr><td>PDBBind</td><td>Jan 2018</td><td>-</td><td>-</td><td>16276</td><td>3312</td></tr><tr><td>PDSP Ki</td><td>2019</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table></div></div></div> <a id="225532495" scrollto-destination="225532495"></a> <div data-id="f5" data-content-id="f5" class="fig fig-section js-fig-section" swap-content-for-modal="true"><div class="graphic-wrap"><img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/m_bbz157f5.jpeg?Expires=1734462476&Signature=IFQfzd4Up2tgAVJvqeuo~Hg-fdUiMUcnwcmC2gW3x2pO28GVC8~Qk26ljHUTH54atAfUXKoy1EEkGII2Ty5rcmZ6TYK4AjPv9DtJU14LOATYXf3cG0zdKvyWiEZ5dM5LRIDeNh-3Taag4psW7BfvGIoXfr8bb~m0CikzA~tD26aFfaIhyf7O5ITaigPuJKyR2Wp6xVl5EX5v8ZWEq9KtID-QwBfiWQ8Y9o2n2MIjwIY2DMsRSHfjxKHPytiQuKoDwid8Q985EFAYvUk~eyHF0X2UcQN6nu3p1HA2kwGdvHRSzOphcH2jNhHSA0PcaU0cC4MmTj~iyPtHUMQOfVTV-g__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="Coupled matrix–matrix versus coupled tensor–matrix." data-path-from-xml="bbz157f5.tif" /><div class="graphic-bottom"><div class="label fig-label" id="label-225532495"><strong>Figure 5</strong></div><div class="caption fig-caption"><p class="chapter-para">Coupled matrix–matrix versus coupled tensor–matrix.</p></div><div class="ajax-articleAbstract-exclude-regex fig-orig original-slide figure-button-wrap"><a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" role="button" aria-describedby="label-225532495" href="/view-large/figure/225532495/bbz157f5.tif" data-path-from-xml="bbz157f5.tif" target="_blank">Open in new tab</a><a class="download-slide" role="button" aria-describedby="label-225532495" data-section="225532495" href="/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/22/1/10.1093_bib_bbz157/2/bbz157f5.jpeg?Expires=1734462476&Signature=e-DVE9qo5UdYE0zzdXmyO3KgIvwvHrFCiUzIw9yY0hJukaz~FMTYsPNdhcdb7oI~EjPZLtAJtXmN9vSlxJDJmi-0o7x69umA-GAap-qy4aLKVR-O-fl5uPfuWMet8EIPlekgPIhuIGQ3Xh5WmJdy4OoOGwmSNaJmspEfzCDT40hmIxKjMd9YCQbMkweMw1vLn8-0AI-nLQJKI8sLILZHST7MseOmheYV7~3K5e0yI2fSgUKrmvrdMHvVTECl4JaBHInEvfy46JSHEkQoe9PrEHPAA1HPIWPcEpljyz3DwO~TrCIzlSQMpJSuHKB905fjfKvE0lEBVcEtagAUICMfeA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=225532495&ar=5681786&xsltPath=~/UI/app/XSLT&imagename=&siteId=5143" data-path-from-xml="bbz157f5.tif">Download slide</a></div></div></div></div> <a id="225532496" scrollto-destination="225532496"></a> <div content-id="TB11" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB11" data-id="TB11"><span class="label title-label" id="label-81584">Table 11</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532496" aria-describedby="label-81584"> Open in new tab </a></div><div class="caption caption-id-" id="caption-81584"><p class="chapter-para">The summary of all algorithms and databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-81584" aria-describedby="
 caption-81584"><thead><tr><th><strong>Study</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithm</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>Bock <em>et al.</em> [<span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>]</td><td>SVM</td><td>PDSP Ki, Swiss-Prot (UniProt), Ligand.Info, ExPASy</td></tr><tr><td>Faulon <em>et al.</em> [<span class="xrefLink" id="jumplink-ref130"></span><a href="javascript:;" reveal-id="ref130" data-open="ref130" class="link link-ref link-reveal xref-bibr">130</a>]</td><td>SVM</td><td>PTC, KEGG, DrugBank</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref129"></span><a href="javascript:;" reveal-id="ref129" data-open="ref129" class="link link-ref link-reveal xref-bibr">129</a>]</td><td>SVM</td><td>DrugBank, UniProt, PubChem, PDSP Ki, GLIDA</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref127"></span><a href="javascript:;" reveal-id="ref127" data-open="ref127" class="link link-ref link-reveal xref-bibr">127</a>]</td><td>SVM</td><td>DrugBank, UniProt, NIST05, CE-MS</td></tr><tr><td>Wassermann <em>et al.</em> [<span class="xrefLink" id="jumplink-ref128"></span><a href="javascript:;" reveal-id="ref128" data-open="ref128" class="link link-ref link-reveal xref-bibr">128</a>]</td><td>SVM</td><td>MEROPS, CutDB, SCOP, MDDR, PDB, BindingDB</td></tr><tr><td>Jacob <em>et al.</em> [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>]</td><td>SVM</td><td>KEGG BRITE</td></tr><tr><td>Cao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref138"></span><a href="javascript:;" reveal-id="ref138" data-open="ref138" class="link link-ref link-reveal xref-bibr">138</a>]</td><td>SVM</td><td>DrugBank, Matador, STITCH, PubChem, SIDER</td></tr><tr><td>Mousavian <em>et al.</em> [<span class="xrefLink" id="jumplink-ref136"></span><a href="javascript:;" reveal-id="ref136" data-open="ref136" class="link link-ref link-reveal xref-bibr">136</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref135"></span><a href="javascript:;" reveal-id="ref135" data-open="ref135" class="link link-ref link-reveal xref-bibr">135</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ding <em>et al.</em> [<span class="xrefLink" id="jumplink-ref134"></span><a href="javascript:;" reveal-id="ref134" data-open="ref134" class="link link-ref link-reveal xref-bibr">134</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, Matador</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>]</td><td>BGL</td><td>KEGG DRUG, KEGG LIGAND, KEGG GENES, KEGG BRITE, BRENDA, SuperTarget, DrugBank, JAPIC</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>]</td><td>BGL or KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bleakley <em>et al.</em> [<span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>]</td><td>BLM, KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>]</td><td>NN</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Xia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>]</td><td>LaRLS, NetLapRLS</td><td>KEGG LIGAND, KEGG GENES</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>]</td><td>GIP, RLS</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Perlman <em>et al.</em> [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>]</td><td>SITAR</td><td>KEGG DRUG, DrugBank, DCDB, SuperTarget, REACTOME, CTD</td></tr><tr><td>Takarabe <em>et al.</em> [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>]</td><td>PKR</td><td>AERS, SIDER, JAPIC, KEGG DRUG, KEGG GENES</td></tr><tr><td>Gonen [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>]</td><td>KBMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>]</td><td>NBI, TBSI, DBSI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>]</td><td>NRWRH</td><td>KEGG LIGAND, KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Mei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>]</td><td>BLM-NII</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>]</td><td>SVM, RF</td><td>DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>]</td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>]</td><td>RBM</td><td>MATADOR, STITCH</td></tr><tr><td>Zheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>]</td><td>MSCMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>]</td><td>WNN-GIP</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cobanoglu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>]</td><td>PMF</td><td>DrugBank</td></tr><tr><td>Alaimo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>]</td><td>DT-Hybrid</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>])</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>]</td><td>NetCBP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref139"></span><a href="javascript:;" reveal-id="ref139" data-open="ref139" class="link link-ref link-reveal xref-bibr">139</a>]</td><td>MH-SVM</td><td>STITCH, PubChem, UniProt, PFAM</td></tr><tr><td>Pahikkala <em>et al.</em> [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>]</td><td>RF, RLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Niu et al. [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>]</td><td>EnsemRF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bharadwaja [<span class="xrefLink" id="jumplink-ref156"></span><a href="javascript:;" reveal-id="ref156" data-open="ref156" class="link link-ref link-reveal xref-bibr">156</a>]</td><td>KRLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref155"></span><a href="javascript:;" reveal-id="ref155" data-open="ref155" class="link link-ref link-reveal xref-bibr">155</a>]</td><td>RLS-Kron</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Peng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>]</td><td>NormMulInf</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>]</td><td>EnsemSTACK</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Seal <em>et al.</em> [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>]</td><td>RWR</td><td>DrugBank, ChEMBL</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>]</td><td>Super-Target Clustering</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>]</td><td>SRP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>]</td><td>PUDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>]</td><td>NRLMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, KEGG LIGAND</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]</td><td>EnsemDT</td><td>DrugBank</td></tr><tr><td>Ba-alawi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>]</td><td>DASPfind</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yuan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>]</td><td>DrugE-Rank</td><td>DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref157"></span><a href="javascript:;" reveal-id="ref157" data-open="ref157" class="link link-ref link-reveal xref-bibr">157</a>]</td><td>RLS-KF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Nascimento <em>et al.</em> [<span class="xrefLink" id="jumplink-ref158"></span><a href="javascript:;" reveal-id="ref158" data-open="ref158" class="link link-ref link-reveal xref-bibr">158</a>]</td><td>KronRLS-MKL</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lim <em>et al.</em> [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>]</td><td>COSINE</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Buza <em>et al.</em> [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>]</td><td>ECkNN, HLM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase, KEGG GENES</td></tr><tr><td>Peska <em>et al.</em> [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]</td><td>BPR, BRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Meng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>]</td><td>PDTPS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>]</td><td>LPLNI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref172"></span><a href="javascript:;" reveal-id="ref172" data-open="ref172" class="link link-ref link-reveal xref-bibr">172</a>, <span class="xrefLink" id="jumplink-ref173"></span><a href="javascript:;" reveal-id="ref173" data-open="ref173" class="link link-ref link-reveal xref-bibr">173</a>]</td><td>EnsemDT, EnsemKRR</td><td>DrugBank ([<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]), KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>]</td><td>GRMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>]</td><td>KMDR</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Olayan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>]</td><td>RF (DDR)</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>]</td><td>MultiviewDTI</td><td>DrugBank</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>]</td><td>LRE</td><td>DrugBank, KEGG</td></tr><tr><td>Wen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>]</td><td>DeepDTIs</td><td>DrugBank</td></tr><tr><td>Luo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>]</td><td>DTINet</td><td>DrugBank, HPRD</td></tr><tr><td>Zong <em>et al.</em> [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>]</td><td>DeepWalk</td><td>DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>]</td><td>SimBoost, SimBoostQuant</td><td>Kinome Datasets in [<span class="xrefLink" id="jumplink-ref307"></span><a href="javascript:;" reveal-id="ref307" data-open="ref307" class="link link-ref link-reveal xref-bibr">307</a>, <span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>]</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>]</td><td>DVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref164"></span><a href="javascript:;" reveal-id="ref164" data-open="ref164" class="link link-ref link-reveal xref-bibr">164</a>]</td><td>DrugRPE</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>])</td></tr><tr><td>Rayhan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>]</td><td>iDTI-ESBoost</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref233"></span><a href="javascript:;" reveal-id="ref233" data-open="ref233" class="link link-ref link-reveal xref-bibr">233</a>]</td><td>DNILMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG GENES, KEGG DRUG, KEGG COMPOUND</td></tr><tr><td>Ohue <em>et al.</em> [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>]</td><td>CGBVS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>]</td><td>DLGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Sharma <em>et al.</em> [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>]</td><td>BE-DTI’</td><td>DrugBank, KEGG</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>]</td><td>WBRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>]</td><td>IN-RWR, Co-rank</td><td>DrugBank, DGIdb, TTD</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>]</td><td>LRF-DTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kadiyala [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>]</td><td>WLNM</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Manoochehri <em>et al.</em> [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>]</td><td>DMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Mongia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>]</td><td>MGRNNM, DGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref207"></span><a href="javascript:;" reveal-id="ref207" data-open="ref207" class="link link-ref link-reveal xref-bibr">207</a>]</td><td>NeoDTI</td><td>DrugBank, HPRD ([<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>])</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>]</td><td>AutoDNP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>]</td><td>Pseudo-SMR</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>]</td><td>RFDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ban <em>et al.</em> [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>]</td><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Bolgar <em>et al.</em> [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>]</td><td>VB-MK-LMF</td><td>KEGG DRUG,KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lee <em>et al.</em>[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>]</td><td>DeepConv-DTI</td><td>DrugBank 4.0 [<span class="xrefLink" id="jumplink-ref243"></span><a href="javascript:;" reveal-id="ref243" data-open="ref243" class="link link-ref link-reveal xref-bibr">243</a>],KEGG, International Union of Basic and Clinical Pharmacology (IUPHAR) [<span class="xrefLink" id="jumplink-ref309"></span><a href="javascript:;" reveal-id="ref309" data-open="ref309" class="link link-ref link-reveal xref-bibr">309</a>]</td></tr><tr><td>You <em>et al.</em> [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>]</td><td>LASSO-DNN</td><td>Drugbank</td></tr><tr><td>Özgür <em>et al.</em> [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>]</td><td>DeepDTA</td><td>Kinase [<span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>], KIBA [<span class="xrefLink" id="jumplink-ref310"></span><a href="javascript:;" reveal-id="ref310" data-open="ref310" class="link link-ref link-reveal xref-bibr">310</a>]</td></tr><tr><td>Gao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>]</td><td>DeepNP</td><td>BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>]</td></tr><tr><td>Xie <em>et al.</em> [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>]</td><td>DeepTrans</td><td>DrugBank</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Study</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithm</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>Bock <em>et al.</em> [<span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>]</td><td>SVM</td><td>PDSP Ki, Swiss-Prot (UniProt), Ligand.Info, ExPASy</td></tr><tr><td>Faulon <em>et al.</em> [<span class="xrefLink" id="jumplink-ref130"></span><a href="javascript:;" reveal-id="ref130" data-open="ref130" class="link link-ref link-reveal xref-bibr">130</a>]</td><td>SVM</td><td>PTC, KEGG, DrugBank</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref129"></span><a href="javascript:;" reveal-id="ref129" data-open="ref129" class="link link-ref link-reveal xref-bibr">129</a>]</td><td>SVM</td><td>DrugBank, UniProt, PubChem, PDSP Ki, GLIDA</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref127"></span><a href="javascript:;" reveal-id="ref127" data-open="ref127" class="link link-ref link-reveal xref-bibr">127</a>]</td><td>SVM</td><td>DrugBank, UniProt, NIST05, CE-MS</td></tr><tr><td>Wassermann <em>et al.</em> [<span class="xrefLink" id="jumplink-ref128"></span><a href="javascript:;" reveal-id="ref128" data-open="ref128" class="link link-ref link-reveal xref-bibr">128</a>]</td><td>SVM</td><td>MEROPS, CutDB, SCOP, MDDR, PDB, BindingDB</td></tr><tr><td>Jacob <em>et al.</em> [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>]</td><td>SVM</td><td>KEGG BRITE</td></tr><tr><td>Cao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref138"></span><a href="javascript:;" reveal-id="ref138" data-open="ref138" class="link link-ref link-reveal xref-bibr">138</a>]</td><td>SVM</td><td>DrugBank, Matador, STITCH, PubChem, SIDER</td></tr><tr><td>Mousavian <em>et al.</em> [<span class="xrefLink" id="jumplink-ref136"></span><a href="javascript:;" reveal-id="ref136" data-open="ref136" class="link link-ref link-reveal xref-bibr">136</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref135"></span><a href="javascript:;" reveal-id="ref135" data-open="ref135" class="link link-ref link-reveal xref-bibr">135</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ding <em>et al.</em> [<span class="xrefLink" id="jumplink-ref134"></span><a href="javascript:;" reveal-id="ref134" data-open="ref134" class="link link-ref link-reveal xref-bibr">134</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, Matador</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>]</td><td>BGL</td><td>KEGG DRUG, KEGG LIGAND, KEGG GENES, KEGG BRITE, BRENDA, SuperTarget, DrugBank, JAPIC</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>]</td><td>BGL or KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bleakley <em>et al.</em> [<span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>]</td><td>BLM, KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>]</td><td>NN</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Xia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>]</td><td>LaRLS, NetLapRLS</td><td>KEGG LIGAND, KEGG GENES</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>]</td><td>GIP, RLS</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Perlman <em>et al.</em> [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>]</td><td>SITAR</td><td>KEGG DRUG, DrugBank, DCDB, SuperTarget, REACTOME, CTD</td></tr><tr><td>Takarabe <em>et al.</em> [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>]</td><td>PKR</td><td>AERS, SIDER, JAPIC, KEGG DRUG, KEGG GENES</td></tr><tr><td>Gonen [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>]</td><td>KBMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>]</td><td>NBI, TBSI, DBSI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>]</td><td>NRWRH</td><td>KEGG LIGAND, KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Mei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>]</td><td>BLM-NII</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>]</td><td>SVM, RF</td><td>DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>]</td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>]</td><td>RBM</td><td>MATADOR, STITCH</td></tr><tr><td>Zheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>]</td><td>MSCMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>]</td><td>WNN-GIP</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cobanoglu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>]</td><td>PMF</td><td>DrugBank</td></tr><tr><td>Alaimo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>]</td><td>DT-Hybrid</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>])</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>]</td><td>NetCBP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref139"></span><a href="javascript:;" reveal-id="ref139" data-open="ref139" class="link link-ref link-reveal xref-bibr">139</a>]</td><td>MH-SVM</td><td>STITCH, PubChem, UniProt, PFAM</td></tr><tr><td>Pahikkala <em>et al.</em> [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>]</td><td>RF, RLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Niu et al. [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>]</td><td>EnsemRF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bharadwaja [<span class="xrefLink" id="jumplink-ref156"></span><a href="javascript:;" reveal-id="ref156" data-open="ref156" class="link link-ref link-reveal xref-bibr">156</a>]</td><td>KRLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref155"></span><a href="javascript:;" reveal-id="ref155" data-open="ref155" class="link link-ref link-reveal xref-bibr">155</a>]</td><td>RLS-Kron</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Peng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>]</td><td>NormMulInf</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>]</td><td>EnsemSTACK</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Seal <em>et al.</em> [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>]</td><td>RWR</td><td>DrugBank, ChEMBL</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>]</td><td>Super-Target Clustering</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>]</td><td>SRP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>]</td><td>PUDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>]</td><td>NRLMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, KEGG LIGAND</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]</td><td>EnsemDT</td><td>DrugBank</td></tr><tr><td>Ba-alawi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>]</td><td>DASPfind</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yuan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>]</td><td>DrugE-Rank</td><td>DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref157"></span><a href="javascript:;" reveal-id="ref157" data-open="ref157" class="link link-ref link-reveal xref-bibr">157</a>]</td><td>RLS-KF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Nascimento <em>et al.</em> [<span class="xrefLink" id="jumplink-ref158"></span><a href="javascript:;" reveal-id="ref158" data-open="ref158" class="link link-ref link-reveal xref-bibr">158</a>]</td><td>KronRLS-MKL</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lim <em>et al.</em> [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>]</td><td>COSINE</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Buza <em>et al.</em> [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>]</td><td>ECkNN, HLM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase, KEGG GENES</td></tr><tr><td>Peska <em>et al.</em> [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]</td><td>BPR, BRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Meng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>]</td><td>PDTPS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>]</td><td>LPLNI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref172"></span><a href="javascript:;" reveal-id="ref172" data-open="ref172" class="link link-ref link-reveal xref-bibr">172</a>, <span class="xrefLink" id="jumplink-ref173"></span><a href="javascript:;" reveal-id="ref173" data-open="ref173" class="link link-ref link-reveal xref-bibr">173</a>]</td><td>EnsemDT, EnsemKRR</td><td>DrugBank ([<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]), KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>]</td><td>GRMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>]</td><td>KMDR</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Olayan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>]</td><td>RF (DDR)</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>]</td><td>MultiviewDTI</td><td>DrugBank</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>]</td><td>LRE</td><td>DrugBank, KEGG</td></tr><tr><td>Wen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>]</td><td>DeepDTIs</td><td>DrugBank</td></tr><tr><td>Luo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>]</td><td>DTINet</td><td>DrugBank, HPRD</td></tr><tr><td>Zong <em>et al.</em> [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>]</td><td>DeepWalk</td><td>DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>]</td><td>SimBoost, SimBoostQuant</td><td>Kinome Datasets in [<span class="xrefLink" id="jumplink-ref307"></span><a href="javascript:;" reveal-id="ref307" data-open="ref307" class="link link-ref link-reveal xref-bibr">307</a>, <span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>]</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>]</td><td>DVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref164"></span><a href="javascript:;" reveal-id="ref164" data-open="ref164" class="link link-ref link-reveal xref-bibr">164</a>]</td><td>DrugRPE</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>])</td></tr><tr><td>Rayhan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>]</td><td>iDTI-ESBoost</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref233"></span><a href="javascript:;" reveal-id="ref233" data-open="ref233" class="link link-ref link-reveal xref-bibr">233</a>]</td><td>DNILMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG GENES, KEGG DRUG, KEGG COMPOUND</td></tr><tr><td>Ohue <em>et al.</em> [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>]</td><td>CGBVS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>]</td><td>DLGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Sharma <em>et al.</em> [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>]</td><td>BE-DTI’</td><td>DrugBank, KEGG</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>]</td><td>WBRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>]</td><td>IN-RWR, Co-rank</td><td>DrugBank, DGIdb, TTD</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>]</td><td>LRF-DTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kadiyala [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>]</td><td>WLNM</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Manoochehri <em>et al.</em> [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>]</td><td>DMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Mongia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>]</td><td>MGRNNM, DGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref207"></span><a href="javascript:;" reveal-id="ref207" data-open="ref207" class="link link-ref link-reveal xref-bibr">207</a>]</td><td>NeoDTI</td><td>DrugBank, HPRD ([<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>])</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>]</td><td>AutoDNP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>]</td><td>Pseudo-SMR</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>]</td><td>RFDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ban <em>et al.</em> [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>]</td><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Bolgar <em>et al.</em> [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>]</td><td>VB-MK-LMF</td><td>KEGG DRUG,KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lee <em>et al.</em>[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>]</td><td>DeepConv-DTI</td><td>DrugBank 4.0 [<span class="xrefLink" id="jumplink-ref243"></span><a href="javascript:;" reveal-id="ref243" data-open="ref243" class="link link-ref link-reveal xref-bibr">243</a>],KEGG, International Union of Basic and Clinical Pharmacology (IUPHAR) [<span class="xrefLink" id="jumplink-ref309"></span><a href="javascript:;" reveal-id="ref309" data-open="ref309" class="link link-ref link-reveal xref-bibr">309</a>]</td></tr><tr><td>You <em>et al.</em> [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>]</td><td>LASSO-DNN</td><td>Drugbank</td></tr><tr><td>Özgür <em>et al.</em> [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>]</td><td>DeepDTA</td><td>Kinase [<span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>], KIBA [<span class="xrefLink" id="jumplink-ref310"></span><a href="javascript:;" reveal-id="ref310" data-open="ref310" class="link link-ref link-reveal xref-bibr">310</a>]</td></tr><tr><td>Gao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>]</td><td>DeepNP</td><td>BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>]</td></tr><tr><td>Xie <em>et al.</em> [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>]</td><td>DeepTrans</td><td>DrugBank</td></tr></tbody></table></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB11" data-id="TB11"><span class="label title-label" id="label-81584">Table 11</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/225532496" aria-describedby="label-81584"> Open in new tab </a></div><div class="caption caption-id-" id="caption-81584"><p class="chapter-para">The summary of all algorithms and databases</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-81584" aria-describedby="
 caption-81584"><thead><tr><th><strong>Study</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithm</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>Bock <em>et al.</em> [<span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>]</td><td>SVM</td><td>PDSP Ki, Swiss-Prot (UniProt), Ligand.Info, ExPASy</td></tr><tr><td>Faulon <em>et al.</em> [<span class="xrefLink" id="jumplink-ref130"></span><a href="javascript:;" reveal-id="ref130" data-open="ref130" class="link link-ref link-reveal xref-bibr">130</a>]</td><td>SVM</td><td>PTC, KEGG, DrugBank</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref129"></span><a href="javascript:;" reveal-id="ref129" data-open="ref129" class="link link-ref link-reveal xref-bibr">129</a>]</td><td>SVM</td><td>DrugBank, UniProt, PubChem, PDSP Ki, GLIDA</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref127"></span><a href="javascript:;" reveal-id="ref127" data-open="ref127" class="link link-ref link-reveal xref-bibr">127</a>]</td><td>SVM</td><td>DrugBank, UniProt, NIST05, CE-MS</td></tr><tr><td>Wassermann <em>et al.</em> [<span class="xrefLink" id="jumplink-ref128"></span><a href="javascript:;" reveal-id="ref128" data-open="ref128" class="link link-ref link-reveal xref-bibr">128</a>]</td><td>SVM</td><td>MEROPS, CutDB, SCOP, MDDR, PDB, BindingDB</td></tr><tr><td>Jacob <em>et al.</em> [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>]</td><td>SVM</td><td>KEGG BRITE</td></tr><tr><td>Cao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref138"></span><a href="javascript:;" reveal-id="ref138" data-open="ref138" class="link link-ref link-reveal xref-bibr">138</a>]</td><td>SVM</td><td>DrugBank, Matador, STITCH, PubChem, SIDER</td></tr><tr><td>Mousavian <em>et al.</em> [<span class="xrefLink" id="jumplink-ref136"></span><a href="javascript:;" reveal-id="ref136" data-open="ref136" class="link link-ref link-reveal xref-bibr">136</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref135"></span><a href="javascript:;" reveal-id="ref135" data-open="ref135" class="link link-ref link-reveal xref-bibr">135</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ding <em>et al.</em> [<span class="xrefLink" id="jumplink-ref134"></span><a href="javascript:;" reveal-id="ref134" data-open="ref134" class="link link-ref link-reveal xref-bibr">134</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, Matador</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>]</td><td>BGL</td><td>KEGG DRUG, KEGG LIGAND, KEGG GENES, KEGG BRITE, BRENDA, SuperTarget, DrugBank, JAPIC</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>]</td><td>BGL or KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bleakley <em>et al.</em> [<span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>]</td><td>BLM, KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>]</td><td>NN</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Xia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>]</td><td>LaRLS, NetLapRLS</td><td>KEGG LIGAND, KEGG GENES</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>]</td><td>GIP, RLS</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Perlman <em>et al.</em> [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>]</td><td>SITAR</td><td>KEGG DRUG, DrugBank, DCDB, SuperTarget, REACTOME, CTD</td></tr><tr><td>Takarabe <em>et al.</em> [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>]</td><td>PKR</td><td>AERS, SIDER, JAPIC, KEGG DRUG, KEGG GENES</td></tr><tr><td>Gonen [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>]</td><td>KBMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>]</td><td>NBI, TBSI, DBSI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>]</td><td>NRWRH</td><td>KEGG LIGAND, KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Mei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>]</td><td>BLM-NII</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>]</td><td>SVM, RF</td><td>DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>]</td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>]</td><td>RBM</td><td>MATADOR, STITCH</td></tr><tr><td>Zheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>]</td><td>MSCMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>]</td><td>WNN-GIP</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cobanoglu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>]</td><td>PMF</td><td>DrugBank</td></tr><tr><td>Alaimo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>]</td><td>DT-Hybrid</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>])</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>]</td><td>NetCBP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref139"></span><a href="javascript:;" reveal-id="ref139" data-open="ref139" class="link link-ref link-reveal xref-bibr">139</a>]</td><td>MH-SVM</td><td>STITCH, PubChem, UniProt, PFAM</td></tr><tr><td>Pahikkala <em>et al.</em> [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>]</td><td>RF, RLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Niu et al. [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>]</td><td>EnsemRF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bharadwaja [<span class="xrefLink" id="jumplink-ref156"></span><a href="javascript:;" reveal-id="ref156" data-open="ref156" class="link link-ref link-reveal xref-bibr">156</a>]</td><td>KRLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref155"></span><a href="javascript:;" reveal-id="ref155" data-open="ref155" class="link link-ref link-reveal xref-bibr">155</a>]</td><td>RLS-Kron</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Peng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>]</td><td>NormMulInf</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>]</td><td>EnsemSTACK</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Seal <em>et al.</em> [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>]</td><td>RWR</td><td>DrugBank, ChEMBL</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>]</td><td>Super-Target Clustering</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>]</td><td>SRP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>]</td><td>PUDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>]</td><td>NRLMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, KEGG LIGAND</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]</td><td>EnsemDT</td><td>DrugBank</td></tr><tr><td>Ba-alawi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>]</td><td>DASPfind</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yuan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>]</td><td>DrugE-Rank</td><td>DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref157"></span><a href="javascript:;" reveal-id="ref157" data-open="ref157" class="link link-ref link-reveal xref-bibr">157</a>]</td><td>RLS-KF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Nascimento <em>et al.</em> [<span class="xrefLink" id="jumplink-ref158"></span><a href="javascript:;" reveal-id="ref158" data-open="ref158" class="link link-ref link-reveal xref-bibr">158</a>]</td><td>KronRLS-MKL</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lim <em>et al.</em> [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>]</td><td>COSINE</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Buza <em>et al.</em> [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>]</td><td>ECkNN, HLM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase, KEGG GENES</td></tr><tr><td>Peska <em>et al.</em> [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]</td><td>BPR, BRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Meng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>]</td><td>PDTPS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>]</td><td>LPLNI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref172"></span><a href="javascript:;" reveal-id="ref172" data-open="ref172" class="link link-ref link-reveal xref-bibr">172</a>, <span class="xrefLink" id="jumplink-ref173"></span><a href="javascript:;" reveal-id="ref173" data-open="ref173" class="link link-ref link-reveal xref-bibr">173</a>]</td><td>EnsemDT, EnsemKRR</td><td>DrugBank ([<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]), KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>]</td><td>GRMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>]</td><td>KMDR</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Olayan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>]</td><td>RF (DDR)</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>]</td><td>MultiviewDTI</td><td>DrugBank</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>]</td><td>LRE</td><td>DrugBank, KEGG</td></tr><tr><td>Wen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>]</td><td>DeepDTIs</td><td>DrugBank</td></tr><tr><td>Luo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>]</td><td>DTINet</td><td>DrugBank, HPRD</td></tr><tr><td>Zong <em>et al.</em> [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>]</td><td>DeepWalk</td><td>DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>]</td><td>SimBoost, SimBoostQuant</td><td>Kinome Datasets in [<span class="xrefLink" id="jumplink-ref307"></span><a href="javascript:;" reveal-id="ref307" data-open="ref307" class="link link-ref link-reveal xref-bibr">307</a>, <span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>]</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>]</td><td>DVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref164"></span><a href="javascript:;" reveal-id="ref164" data-open="ref164" class="link link-ref link-reveal xref-bibr">164</a>]</td><td>DrugRPE</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>])</td></tr><tr><td>Rayhan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>]</td><td>iDTI-ESBoost</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref233"></span><a href="javascript:;" reveal-id="ref233" data-open="ref233" class="link link-ref link-reveal xref-bibr">233</a>]</td><td>DNILMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG GENES, KEGG DRUG, KEGG COMPOUND</td></tr><tr><td>Ohue <em>et al.</em> [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>]</td><td>CGBVS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>]</td><td>DLGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Sharma <em>et al.</em> [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>]</td><td>BE-DTI’</td><td>DrugBank, KEGG</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>]</td><td>WBRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>]</td><td>IN-RWR, Co-rank</td><td>DrugBank, DGIdb, TTD</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>]</td><td>LRF-DTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kadiyala [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>]</td><td>WLNM</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Manoochehri <em>et al.</em> [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>]</td><td>DMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Mongia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>]</td><td>MGRNNM, DGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref207"></span><a href="javascript:;" reveal-id="ref207" data-open="ref207" class="link link-ref link-reveal xref-bibr">207</a>]</td><td>NeoDTI</td><td>DrugBank, HPRD ([<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>])</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>]</td><td>AutoDNP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>]</td><td>Pseudo-SMR</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>]</td><td>RFDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ban <em>et al.</em> [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>]</td><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Bolgar <em>et al.</em> [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>]</td><td>VB-MK-LMF</td><td>KEGG DRUG,KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lee <em>et al.</em>[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>]</td><td>DeepConv-DTI</td><td>DrugBank 4.0 [<span class="xrefLink" id="jumplink-ref243"></span><a href="javascript:;" reveal-id="ref243" data-open="ref243" class="link link-ref link-reveal xref-bibr">243</a>],KEGG, International Union of Basic and Clinical Pharmacology (IUPHAR) [<span class="xrefLink" id="jumplink-ref309"></span><a href="javascript:;" reveal-id="ref309" data-open="ref309" class="link link-ref link-reveal xref-bibr">309</a>]</td></tr><tr><td>You <em>et al.</em> [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>]</td><td>LASSO-DNN</td><td>Drugbank</td></tr><tr><td>Özgür <em>et al.</em> [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>]</td><td>DeepDTA</td><td>Kinase [<span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>], KIBA [<span class="xrefLink" id="jumplink-ref310"></span><a href="javascript:;" reveal-id="ref310" data-open="ref310" class="link link-ref link-reveal xref-bibr">310</a>]</td></tr><tr><td>Gao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>]</td><td>DeepNP</td><td>BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>]</td></tr><tr><td>Xie <em>et al.</em> [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>]</td><td>DeepTrans</td><td>DrugBank</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><th><strong>Study</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>Algorithm</strong><span aria-hidden="true" style="display: none;"> . </span></th><th><strong>D</strong>atabase<span aria-hidden="true" style="display: none;"> . </span></th></tr></thead><tbody><tr><td>Bock <em>et al.</em> [<span class="xrefLink" id="jumplink-ref78"></span><a href="javascript:;" reveal-id="ref78" data-open="ref78" class="link link-ref link-reveal xref-bibr">78</a>]</td><td>SVM</td><td>PDSP Ki, Swiss-Prot (UniProt), Ligand.Info, ExPASy</td></tr><tr><td>Faulon <em>et al.</em> [<span class="xrefLink" id="jumplink-ref130"></span><a href="javascript:;" reveal-id="ref130" data-open="ref130" class="link link-ref link-reveal xref-bibr">130</a>]</td><td>SVM</td><td>PTC, KEGG, DrugBank</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref129"></span><a href="javascript:;" reveal-id="ref129" data-open="ref129" class="link link-ref link-reveal xref-bibr">129</a>]</td><td>SVM</td><td>DrugBank, UniProt, PubChem, PDSP Ki, GLIDA</td></tr><tr><td>Nagamine <em>et al.</em> [<span class="xrefLink" id="jumplink-ref127"></span><a href="javascript:;" reveal-id="ref127" data-open="ref127" class="link link-ref link-reveal xref-bibr">127</a>]</td><td>SVM</td><td>DrugBank, UniProt, NIST05, CE-MS</td></tr><tr><td>Wassermann <em>et al.</em> [<span class="xrefLink" id="jumplink-ref128"></span><a href="javascript:;" reveal-id="ref128" data-open="ref128" class="link link-ref link-reveal xref-bibr">128</a>]</td><td>SVM</td><td>MEROPS, CutDB, SCOP, MDDR, PDB, BindingDB</td></tr><tr><td>Jacob <em>et al.</em> [<span class="xrefLink" id="jumplink-ref14"></span><a href="javascript:;" reveal-id="ref14" data-open="ref14" class="link link-ref link-reveal xref-bibr">14</a>]</td><td>SVM</td><td>KEGG BRITE</td></tr><tr><td>Cao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref137"></span><a href="javascript:;" reveal-id="ref137" data-open="ref137" class="link link-ref link-reveal xref-bibr">137</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref138"></span><a href="javascript:;" reveal-id="ref138" data-open="ref138" class="link link-ref link-reveal xref-bibr">138</a>]</td><td>SVM</td><td>DrugBank, Matador, STITCH, PubChem, SIDER</td></tr><tr><td>Mousavian <em>et al.</em> [<span class="xrefLink" id="jumplink-ref136"></span><a href="javascript:;" reveal-id="ref136" data-open="ref136" class="link link-ref link-reveal xref-bibr">136</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref135"></span><a href="javascript:;" reveal-id="ref135" data-open="ref135" class="link link-ref link-reveal xref-bibr">135</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ding <em>et al.</em> [<span class="xrefLink" id="jumplink-ref134"></span><a href="javascript:;" reveal-id="ref134" data-open="ref134" class="link link-ref link-reveal xref-bibr">134</a>]</td><td>SVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, Matador</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref143"></span><a href="javascript:;" reveal-id="ref143" data-open="ref143" class="link link-ref link-reveal xref-bibr">143</a>]</td><td>BGL</td><td>KEGG DRUG, KEGG LIGAND, KEGG GENES, KEGG BRITE, BRENDA, SuperTarget, DrugBank, JAPIC</td></tr><tr><td>Yamanishi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref13"></span><a href="javascript:;" reveal-id="ref13" data-open="ref13" class="link link-ref link-reveal xref-bibr">13</a>]</td><td>BGL or KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bleakley <em>et al.</em> [<span class="xrefLink" id="jumplink-ref101"></span><a href="javascript:;" reveal-id="ref101" data-open="ref101" class="link link-ref link-reveal xref-bibr">101</a>]</td><td>BLM, KRM, NN</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>]</td><td>NN</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Xia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr">6</a>]</td><td>LaRLS, NetLapRLS</td><td>KEGG LIGAND, KEGG GENES</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>]</td><td>GIP, RLS</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Perlman <em>et al.</em> [<span class="xrefLink" id="jumplink-ref95"></span><a href="javascript:;" reveal-id="ref95" data-open="ref95" class="link link-ref link-reveal xref-bibr">95</a>]</td><td>SITAR</td><td>KEGG DRUG, DrugBank, DCDB, SuperTarget, REACTOME, CTD</td></tr><tr><td>Takarabe <em>et al.</em> [<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr">9</a>]</td><td>PKR</td><td>AERS, SIDER, JAPIC, KEGG DRUG, KEGG GENES</td></tr><tr><td>Gonen [<span class="xrefLink" id="jumplink-ref194"></span><a href="javascript:;" reveal-id="ref194" data-open="ref194" class="link link-ref link-reveal xref-bibr">194</a>]</td><td>KBMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>]</td><td>NBI, TBSI, DBSI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref196"></span><a href="javascript:;" reveal-id="ref196" data-open="ref196" class="link link-ref link-reveal xref-bibr">196</a>]</td><td>NRWRH</td><td>KEGG LIGAND, KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Mei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref107"></span><a href="javascript:;" reveal-id="ref107" data-open="ref107" class="link link-ref link-reveal xref-bibr">107</a>]</td><td>BLM-NII</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>]</td><td>SVM, RF</td><td>DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref216"></span><a href="javascript:;" reveal-id="ref216" data-open="ref216" class="link link-ref link-reveal xref-bibr">216</a>]</td><td><span class="inline-formula no-formula-id">|$L_{1}$|</span>-regularized</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref204"></span><a href="javascript:;" reveal-id="ref204" data-open="ref204" class="link link-ref link-reveal xref-bibr">204</a>]</td><td>RBM</td><td>MATADOR, STITCH</td></tr><tr><td>Zheng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref182"></span><a href="javascript:;" reveal-id="ref182" data-open="ref182" class="link link-ref link-reveal xref-bibr">182</a>]</td><td>MSCMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Van Laarhoven <em>et al.</em> [<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>]</td><td>WNN-GIP</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Cobanoglu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref179"></span><a href="javascript:;" reveal-id="ref179" data-open="ref179" class="link link-ref link-reveal xref-bibr">179</a>]</td><td>PMF</td><td>DrugBank</td></tr><tr><td>Alaimo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref209"></span><a href="javascript:;" reveal-id="ref209" data-open="ref209" class="link link-ref link-reveal xref-bibr">209</a>]</td><td>DT-Hybrid</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref195"></span><a href="javascript:;" reveal-id="ref195" data-open="ref195" class="link link-ref link-reveal xref-bibr">195</a>])</td></tr><tr><td>Chen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref111"></span><a href="javascript:;" reveal-id="ref111" data-open="ref111" class="link link-ref link-reveal xref-bibr">111</a>]</td><td>NetCBP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Tabei <em>et al.</em> [<span class="xrefLink" id="jumplink-ref139"></span><a href="javascript:;" reveal-id="ref139" data-open="ref139" class="link link-ref link-reveal xref-bibr">139</a>]</td><td>MH-SVM</td><td>STITCH, PubChem, UniProt, PFAM</td></tr><tr><td>Pahikkala <em>et al.</em> [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>]</td><td>RF, RLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Niu et al. [<span class="xrefLink" id="jumplink-ref112"></span><a href="javascript:;" reveal-id="ref112" data-open="ref112" class="link link-ref link-reveal xref-bibr">112</a>]</td><td>EnsemRF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Bharadwaja [<span class="xrefLink" id="jumplink-ref156"></span><a href="javascript:;" reveal-id="ref156" data-open="ref156" class="link link-ref link-reveal xref-bibr">156</a>]</td><td>KRLS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref155"></span><a href="javascript:;" reveal-id="ref155" data-open="ref155" class="link link-ref link-reveal xref-bibr">155</a>]</td><td>RLS-Kron</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Peng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref199"></span><a href="javascript:;" reveal-id="ref199" data-open="ref199" class="link link-ref link-reveal xref-bibr">199</a>]</td><td>NormMulInf</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang [<span class="xrefLink" id="jumplink-ref176"></span><a href="javascript:;" reveal-id="ref176" data-open="ref176" class="link link-ref link-reveal xref-bibr">176</a>]</td><td>EnsemSTACK</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Seal <em>et al.</em> [<span class="xrefLink" id="jumplink-ref202"></span><a href="javascript:;" reveal-id="ref202" data-open="ref202" class="link link-ref link-reveal xref-bibr">202</a>]</td><td>RWR</td><td>DrugBank, ChEMBL</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref104"></span><a href="javascript:;" reveal-id="ref104" data-open="ref104" class="link link-ref link-reveal xref-bibr">104</a>]</td><td>Super-Target Clustering</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref97"></span><a href="javascript:;" reveal-id="ref97" data-open="ref97" class="link link-ref link-reveal xref-bibr">97</a>]</td><td>SRP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref152"></span><a href="javascript:;" reveal-id="ref152" data-open="ref152" class="link link-ref link-reveal xref-bibr">152</a>]</td><td>PUDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND</td></tr><tr><td>Liu <em>et al.</em> [<span class="xrefLink" id="jumplink-ref187"></span><a href="javascript:;" reveal-id="ref187" data-open="ref187" class="link link-ref link-reveal xref-bibr">187</a>]</td><td>NRLMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, ChEMBL, KEGG LIGAND</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]</td><td>EnsemDT</td><td>DrugBank</td></tr><tr><td>Ba-alawi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref167"></span><a href="javascript:;" reveal-id="ref167" data-open="ref167" class="link link-ref link-reveal xref-bibr">167</a>]</td><td>DASPfind</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Yuan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref177"></span><a href="javascript:;" reveal-id="ref177" data-open="ref177" class="link link-ref link-reveal xref-bibr">177</a>]</td><td>DrugE-Rank</td><td>DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref157"></span><a href="javascript:;" reveal-id="ref157" data-open="ref157" class="link link-ref link-reveal xref-bibr">157</a>]</td><td>RLS-KF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Nascimento <em>et al.</em> [<span class="xrefLink" id="jumplink-ref158"></span><a href="javascript:;" reveal-id="ref158" data-open="ref158" class="link link-ref link-reveal xref-bibr">158</a>]</td><td>KronRLS-MKL</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lim <em>et al.</em> [<span class="xrefLink" id="jumplink-ref218"></span><a href="javascript:;" reveal-id="ref218" data-open="ref218" class="link link-ref link-reveal xref-bibr">218</a>]</td><td>COSINE</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Buza <em>et al.</em> [<span class="xrefLink" id="jumplink-ref98"></span><a href="javascript:;" reveal-id="ref98" data-open="ref98" class="link link-ref link-reveal xref-bibr">98</a>]</td><td>ECkNN, HLM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase, KEGG GENES</td></tr><tr><td>Peska <em>et al.</em> [<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr">2</a>]</td><td>BPR, BRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Meng <em>et al.</em> [<span class="xrefLink" id="jumplink-ref215"></span><a href="javascript:;" reveal-id="ref215" data-open="ref215" class="link link-ref link-reveal xref-bibr">215</a>]</td><td>PDTPS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref105"></span><a href="javascript:;" reveal-id="ref105" data-open="ref105" class="link link-ref link-reveal xref-bibr">105</a>]</td><td>LPLNI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref172"></span><a href="javascript:;" reveal-id="ref172" data-open="ref172" class="link link-ref link-reveal xref-bibr">172</a>, <span class="xrefLink" id="jumplink-ref173"></span><a href="javascript:;" reveal-id="ref173" data-open="ref173" class="link link-ref link-reveal xref-bibr">173</a>]</td><td>EnsemDT, EnsemKRR</td><td>DrugBank ([<span class="xrefLink" id="jumplink-ref171"></span><a href="javascript:;" reveal-id="ref171" data-open="ref171" class="link link-ref link-reveal xref-bibr">171</a>]), KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ezzat <em>et al.</em> [<span class="xrefLink" id="jumplink-ref189"></span><a href="javascript:;" reveal-id="ref189" data-open="ref189" class="link link-ref link-reveal xref-bibr">189</a>]</td><td>GRMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kuang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref208"></span><a href="javascript:;" reveal-id="ref208" data-open="ref208" class="link link-ref link-reveal xref-bibr">208</a>]</td><td>KMDR</td><td>DrugBank, KEGG LIGAND, UniProt</td></tr><tr><td>Olayan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref145"></span><a href="javascript:;" reveal-id="ref145" data-open="ref145" class="link link-ref link-reveal xref-bibr">145</a>]</td><td>RF (DDR)</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref103"></span><a href="javascript:;" reveal-id="ref103" data-open="ref103" class="link link-ref link-reveal xref-bibr">103</a>]</td><td>MultiviewDTI</td><td>DrugBank</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref180"></span><a href="javascript:;" reveal-id="ref180" data-open="ref180" class="link link-ref link-reveal xref-bibr">180</a>]</td><td>LRE</td><td>DrugBank, KEGG</td></tr><tr><td>Wen <em>et al.</em> [<span class="xrefLink" id="jumplink-ref118"></span><a href="javascript:;" reveal-id="ref118" data-open="ref118" class="link link-ref link-reveal xref-bibr">118</a>]</td><td>DeepDTIs</td><td>DrugBank</td></tr><tr><td>Luo <em>et al.</em> [<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>]</td><td>DTINet</td><td>DrugBank, HPRD</td></tr><tr><td>Zong <em>et al.</em> [<span class="xrefLink" id="jumplink-ref117"></span><a href="javascript:;" reveal-id="ref117" data-open="ref117" class="link link-ref link-reveal xref-bibr">117</a>]</td><td>DeepWalk</td><td>DrugBank</td></tr><tr><td>He <em>et al.</em> [<span class="xrefLink" id="jumplink-ref159"></span><a href="javascript:;" reveal-id="ref159" data-open="ref159" class="link link-ref link-reveal xref-bibr">159</a>]</td><td>SimBoost, SimBoostQuant</td><td>Kinome Datasets in [<span class="xrefLink" id="jumplink-ref307"></span><a href="javascript:;" reveal-id="ref307" data-open="ref307" class="link link-ref link-reveal xref-bibr">307</a>, <span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>]</td></tr><tr><td>Li <em>et al.</em> [<span class="xrefLink" id="jumplink-ref169"></span><a href="javascript:;" reveal-id="ref169" data-open="ref169" class="link link-ref link-reveal xref-bibr">169</a>]</td><td>DVM</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Zhang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref164"></span><a href="javascript:;" reveal-id="ref164" data-open="ref164" class="link link-ref link-reveal xref-bibr">164</a>]</td><td>DrugRPE</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref102"></span><a href="javascript:;" reveal-id="ref102" data-open="ref102" class="link link-ref link-reveal xref-bibr">102</a>])</td></tr><tr><td>Rayhan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref151"></span><a href="javascript:;" reveal-id="ref151" data-open="ref151" class="link link-ref link-reveal xref-bibr">151</a>]</td><td>iDTI-ESBoost</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Hao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref233"></span><a href="javascript:;" reveal-id="ref233" data-open="ref233" class="link link-ref link-reveal xref-bibr">233</a>]</td><td>DNILMF</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG GENES, KEGG DRUG, KEGG COMPOUND</td></tr><tr><td>Ohue <em>et al.</em> [<span class="xrefLink" id="jumplink-ref166"></span><a href="javascript:;" reveal-id="ref166" data-open="ref166" class="link link-ref link-reveal xref-bibr">166</a>]</td><td>CGBVS</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref188"></span><a href="javascript:;" reveal-id="ref188" data-open="ref188" class="link link-ref link-reveal xref-bibr">188</a>]</td><td>DLGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Sharma <em>et al.</em> [<span class="xrefLink" id="jumplink-ref178"></span><a href="javascript:;" reveal-id="ref178" data-open="ref178" class="link link-ref link-reveal xref-bibr">178</a>]</td><td>BE-DTI’</td><td>DrugBank, KEGG</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref109"></span><a href="javascript:;" reveal-id="ref109" data-open="ref109" class="link link-ref link-reveal xref-bibr">109</a>]</td><td>WBRDTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, Kinase</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref198"></span><a href="javascript:;" reveal-id="ref198" data-open="ref198" class="link link-ref link-reveal xref-bibr">198</a>]</td><td>IN-RWR, Co-rank</td><td>DrugBank, DGIdb, TTD</td></tr><tr><td>Shi <em>et al.</em> [<span class="xrefLink" id="jumplink-ref144"></span><a href="javascript:;" reveal-id="ref144" data-open="ref144" class="link link-ref link-reveal xref-bibr">144</a>]</td><td>LRF-DTI</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Kadiyala [<span class="xrefLink" id="jumplink-ref214"></span><a href="javascript:;" reveal-id="ref214" data-open="ref214" class="link link-ref link-reveal xref-bibr">214</a>]</td><td>WLNM</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Manoochehri <em>et al.</em> [<span class="xrefLink" id="jumplink-ref219"></span><a href="javascript:;" reveal-id="ref219" data-open="ref219" class="link link-ref link-reveal xref-bibr">219</a>]</td><td>DMF</td><td>KEGG BRITE, KEGG LIGAND, KEGG GENES, BRENDA, SuperTarget, DrugBank ([<span class="xrefLink" id="jumplink-ref106"></span><a href="javascript:;" reveal-id="ref106" data-open="ref106" class="link link-ref link-reveal xref-bibr">106</a>])</td></tr><tr><td>Mongia <em>et al.</em> [<span class="xrefLink" id="jumplink-ref212"></span><a href="javascript:;" reveal-id="ref212" data-open="ref212" class="link link-ref link-reveal xref-bibr">212</a>, <span class="xrefLink" id="jumplink-ref213"></span><a href="javascript:;" reveal-id="ref213" data-open="ref213" class="link link-ref link-reveal xref-bibr">213</a>]</td><td>MGRNNM, DGRMC</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wan <em>et al.</em> [<span class="xrefLink" id="jumplink-ref207"></span><a href="javascript:;" reveal-id="ref207" data-open="ref207" class="link link-ref link-reveal xref-bibr">207</a>]</td><td>NeoDTI</td><td>DrugBank, HPRD ([<span class="xrefLink" id="jumplink-ref197"></span><a href="javascript:;" reveal-id="ref197" data-open="ref197" class="link link-ref link-reveal xref-bibr">197</a>])</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref116"></span><a href="javascript:;" reveal-id="ref116" data-open="ref116" class="link link-ref link-reveal xref-bibr">116</a>]</td><td>AutoDNP</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Huang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref191"></span><a href="javascript:;" reveal-id="ref191" data-open="ref191" class="link link-ref link-reveal xref-bibr">191</a>]</td><td>Pseudo-SMR</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Wang <em>et al.</em> [<span class="xrefLink" id="jumplink-ref161"></span><a href="javascript:;" reveal-id="ref161" data-open="ref161" class="link link-ref link-reveal xref-bibr">161</a>]</td><td>RFDT</td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Ban <em>et al.</em> [<span class="xrefLink" id="jumplink-ref201"></span><a href="javascript:;" reveal-id="ref201" data-open="ref201" class="link link-ref link-reveal xref-bibr">201</a>]</td><td>NRLMF<span class="inline-formula no-formula-id">|$\beta $|</span></td><td>KEGG BRITE, BRENDA, SuperTarget, DrugBank, KEGG LIGAND, KEGG GENES</td></tr><tr><td>Bolgar <em>et al.</em> [<span class="xrefLink" id="jumplink-ref193"></span><a href="javascript:;" reveal-id="ref193" data-open="ref193" class="link link-ref link-reveal xref-bibr">193</a>]</td><td>VB-MK-LMF</td><td>KEGG DRUG,KEGG BRITE, BRENDA, SuperTarget, DrugBank</td></tr><tr><td>Lee <em>et al.</em>[<span class="xrefLink" id="jumplink-ref122"></span><a href="javascript:;" reveal-id="ref122" data-open="ref122" class="link link-ref link-reveal xref-bibr">122</a>]</td><td>DeepConv-DTI</td><td>DrugBank 4.0 [<span class="xrefLink" id="jumplink-ref243"></span><a href="javascript:;" reveal-id="ref243" data-open="ref243" class="link link-ref link-reveal xref-bibr">243</a>],KEGG, International Union of Basic and Clinical Pharmacology (IUPHAR) [<span class="xrefLink" id="jumplink-ref309"></span><a href="javascript:;" reveal-id="ref309" data-open="ref309" class="link link-ref link-reveal xref-bibr">309</a>]</td></tr><tr><td>You <em>et al.</em> [<span class="xrefLink" id="jumplink-ref121"></span><a href="javascript:;" reveal-id="ref121" data-open="ref121" class="link link-ref link-reveal xref-bibr">121</a>]</td><td>LASSO-DNN</td><td>Drugbank</td></tr><tr><td>Özgür <em>et al.</em> [<span class="xrefLink" id="jumplink-ref120"></span><a href="javascript:;" reveal-id="ref120" data-open="ref120" class="link link-ref link-reveal xref-bibr">120</a>]</td><td>DeepDTA</td><td>Kinase [<span class="xrefLink" id="jumplink-ref308"></span><a href="javascript:;" reveal-id="ref308" data-open="ref308" class="link link-ref link-reveal xref-bibr">308</a>], KIBA [<span class="xrefLink" id="jumplink-ref310"></span><a href="javascript:;" reveal-id="ref310" data-open="ref310" class="link link-ref link-reveal xref-bibr">310</a>]</td></tr><tr><td>Gao <em>et al.</em> [<span class="xrefLink" id="jumplink-ref119"></span><a href="javascript:;" reveal-id="ref119" data-open="ref119" class="link link-ref link-reveal xref-bibr">119</a>]</td><td>DeepNP</td><td>BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>]</td></tr><tr><td>Xie <em>et al.</em> [<span class="xrefLink" id="jumplink-ref124"></span><a href="javascript:;" reveal-id="ref124" data-open="ref124" class="link link-ref link-reveal xref-bibr">124</a>]</td><td>DeepTrans</td><td>DrugBank</td></tr></tbody></table></div></div></div> <h4 scrollto-destination=225532497 id="225532497" class="section-title js-splitscreen-section-title" data-legacy-id=sec3v1>3.3.1 BindingDB</h4> <p class="chapter-para">BindingDB [<span class="xrefLink" id="jumplink-ref257"></span><a href="javascript:;" reveal-id="ref257" data-open="ref257" class="link link-ref link-reveal xref-bibr">257</a>, <span class="xrefLink" id="jumplink-ref297 ref298 ref299"></span><a href="javascript:;" reveal-id="ref297 ref298 ref299" data-open="ref297 ref298 ref299" class="link link-ref link-reveal xref-bibr">297–299</a>] is a repository that contains experimental protein–small molecule interaction information. All of these data were extracted from scientific literature and US patents. In addition, other databases (e.g. ChEMBL [<span class="xrefLink" id="jumplink-ref238"></span><a href="javascript:;" reveal-id="ref238" data-open="ref238" class="link link-ref link-reveal xref-bibr">238</a>], PubChem [<span class="xrefLink" id="jumplink-ref296"></span><a href="javascript:;" reveal-id="ref296" data-open="ref296" class="link link-ref link-reveal xref-bibr">296</a>], etc.) are also linked with BindingDB.</p> <h4 scrollto-destination=225532499 id="225532499" class="section-title js-splitscreen-section-title" data-legacy-id=sec3v2>3.3.2 PDBbind</h4> <p class="chapter-para">PDBbind [<span class="xrefLink" id="jumplink-ref300"></span><a href="javascript:;" reveal-id="ref300" data-open="ref300" class="link link-ref link-reveal xref-bibr">300</a>] was first released in 2004 and the purpose of this database is to bridge the gap between protein structural information and energetic properties. The data stored in PDBbind were classified by the biomolecular complex data from PDB. Then, the binding affinity data were collected from the associated literature on PDB. PDBbind has regular updates with the growth of PDB database.</p> <h4 scrollto-destination=225532501 id="225532501" class="section-title js-splitscreen-section-title" data-legacy-id=sec3v3>3.3.3 PDSP Ki</h4> <p class="chapter-para">PDSP Ki [<span class="xrefLink" id="jumplink-ref301"></span><a href="javascript:;" reveal-id="ref301" data-open="ref301" class="link link-ref link-reveal xref-bibr">301</a>] is a public database that stored binding affinities data of drugs/chemical compounds for four different types of proteins, i.e. receptors, neurotransmitter transporters, ion channels and enzymes. This database was developed and maintained by University of North Carolina at Chapel Hill. Search function for both drugs and targets are provided.</p> <h2 scrollto-destination=225532503 id="225532503" class="section-title js-splitscreen-section-title" data-legacy-id=sec4>4 DTI database challenges and future work</h2> <p class="chapter-para">The challenges in making reliable predictions of DTI can be classified into two main categories: the challenges concerning the databases and those concerning computations. Oftentimes, one may overcome the computational difficulties using different prediction methods depending on the nature of the problem. However, major challenges arise due to the source of the databases. Here, we provide some challenges of the first type, also discussed by authors in [<span class="xrefLink" id="jumplink-ref88"></span><a href="javascript:;" reveal-id="ref88" data-open="ref88" class="link link-ref link-reveal xref-bibr">88</a>, <span class="xrefLink" id="jumplink-ref92"></span><a href="javascript:;" reveal-id="ref92" data-open="ref92" class="link link-ref link-reveal xref-bibr">92</a>], followed by some suggestions on how to deal with the challenges in future work.</p> <h3 scrollto-destination=225532505 id="225532505" class="section-title js-splitscreen-section-title" data-legacy-id=sec4a>4.1 Database challenges and future work</h3> <p class="chapter-para">Almost all the methods used in DTI prediction, particularly similarity-based methods, heavily rely on assertions concerning similar drugs and similar targets, the type of database used for the prediction plays a significant role. In terms of databases, lacking a uniform definition of drugs and targets as well as a consistent way of calling and identifying compounds and biomolecules, overlapping with at least one other source in the pool, adopting different identifiers to represent drug and targets are among the main challenges [<span class="xrefLink" id="jumplink-ref88"></span><a href="javascript:;" reveal-id="ref88" data-open="ref88" class="link link-ref link-reveal xref-bibr">88</a>, <span class="xrefLink" id="jumplink-ref92"></span><a href="javascript:;" reveal-id="ref92" data-open="ref92" class="link link-ref link-reveal xref-bibr">92</a>]. Additionally, incorporating heterogenous data in a database is another challenge to be pointed out. Not all the drugs and targets included in a database have 3D structures and GO/PPI sequences, respectively, which makes similarity scores. As a consequence, the resulting data could vary even if the same literature is used.</p><div class="
 block-child-p
 ">Future predictions should rely on more comprehensive internal databases, which would require a significant effort to map and curate data across the sources that utilize different ways to define, name and identify the drugs and targets. From the data perspective, there is an issue of datasets being of a binary nature; i.e. given an interaction matrix <span class="inline-formula no-formula-id">|$X_{n\times m}$|⁠</span>, for <span class="inline-formula no-formula-id">|$i=1,\ldots ,n$|</span> and <span class="inline-formula no-formula-id">|$j=1,\ldots ,m$|⁠</span>, one may define <div class="formula-wrap"><div class="disp-formula" id="jumplink-" content-id=""><div class="tex-math display-math">$$\begin{align*} x_{ij} & =\begin{cases} 1 & \textrm{if drug}\ d_{i}\ \textrm{and target}\ t_{j}\ \textrm{interact}\\ 0 & \textrm{in the absence of any known interaction.} \end{cases} \end{align*}$$</div></div></div>This causes a significant problem. Some of the 0’s in <span class="inline-formula no-formula-id">|$X_{n\times m}$|</span> may be interactions that are yet undiscovered, which may throw off the training process for the different classifiers. Another point is that in reality DT pairs have binding affinities that vary over a spectrum (interactions are not binary on/off). One suggestion to overcome this challenge is to utilize datasets with continuous values representing DT binding affinities. This have been previously proposed by authors in [<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr">5</a>, <span class="xrefLink" id="jumplink-ref131"></span><a href="javascript:;" reveal-id="ref131" data-open="ref131" class="link link-ref link-reveal xref-bibr">131</a>, <span class="xrefLink" id="jumplink-ref153"></span><a href="javascript:;" reveal-id="ref153" data-open="ref153" class="link link-ref link-reveal xref-bibr">153</a>, <span class="xrefLink" id="jumplink-ref302"></span><a href="javascript:;" reveal-id="ref302" data-open="ref302" class="link link-ref link-reveal xref-bibr">302</a>, <span class="xrefLink" id="jumplink-ref303"></span><a href="javascript:;" reveal-id="ref303" data-open="ref303" class="link link-ref link-reveal xref-bibr">303</a>]. Our suggestion is to replace each <span class="inline-formula no-formula-id">|$x_{ij}$|</span> with continuous-valued parameters. Based on the probability of interaction, one may define <span class="inline-formula no-formula-id">|$x_{ij}=\mu $|</span> where <span class="inline-formula no-formula-id">|$\mu \in \left [0,1\right ]$|⁠</span>. 0, as it should, indicates no interaction while 1 denotes complete interaction. Any number within <span class="inline-formula no-formula-id">|$\left (0,1\right )$|</span> represents the probability that drug <span class="inline-formula no-formula-id">|$d_{i}$|</span> and target <span class="inline-formula no-formula-id">|$t_{j}$|</span> interact.</div><p class="chapter-para">The trend of using such continuous-valued datasets may eventually catch on as it is more useful and more meaningful, in the sense that it represents the reality better than the binary datasets that have been used in the majority of previous work in DTI prediction. The main challenge, however, lies in the fact that to date, there is a large number of small molecule compounds that have not yet been used as drugs and for the majority of them, their interaction proles with proteins are still unknown.</p><p class="chapter-para">Future work on DTI predictions could be categorized in two main approaches. Modifications and suggestions toward the databases in general seem inescapable. On the one hand, the databases should be combined together to collect the most complete set of known drug–protein interactions. On the other hand, the sources should regularly be updated and disseminated, which results in improvements and completeness. A larger number of source databases should be integrated to derive the internal database.</p> <h3 scrollto-destination=225532510 id="225532510" class="section-title js-splitscreen-section-title" data-legacy-id=sec4b>4.2 DTI prediction method challenges and future work</h3> <p class="chapter-para">Future research should focus on methods that combine multiple similarities. The ensemble-based models that combine multiple types of similarities are likely to provide more accurate results than the methods that use one similarity. For instance, repurposed drugs have been identified via retrospective clinical analysis (e.g. reviewing side effects), pharmacological analysis or simply serendipity. Given the surprisingly successful early examples (repurposing minoxidil from hypertension to hair loss, sildenafil from angina to erectile dysfunction and thalidomide from morning sickness to multiple myeloma), research is now focusing on how best to adopt a more comprehensive, systematic approach. In addition, a great amount of work is invested to identify molecular drivers of disease development, progression and treatment resistance, providing many candidate targets for drugs across the spectrum of human disease. However, a majority of these molecular drivers have no known drug to target them. Thus, a comprehensive, improved methodology for predicting DTIs would have great benefit. Due to challenges listed in Section <span class="xrefLink" id="jumplink-sec4a"></span><a href="#sec4a" class="sectionLink xref-sec js-xref-sec">4.1</a>, current knowledge of which cellular molecules are targeted by a drug is scarce and is derived from various, sometimes complementary sources.</p><p class="chapter-para">As per the formulation of the problem, appropriate representation of datasets seems crucial for gaining insight and effectiveness in DTI predictions. In Big Data applications it is common that data is sparse (mostly zeros) and partially missing. Missing data imputation, especially in the context of sparse, noisy data, is therefore a central problem. To infer the missing entries from the known ones, reasonable assumptions should be made based on commonly observed challenges in the structure of data. Considering matrix factorization methods in predicting DTIs, a common situation is a matrix with missing entries (such as the famous Netflix problem.) Under the assumption that the completed matrix has low rank, the low-rank matrix completion problem is NP hard and highly non-convex [<span class="xrefLink" id="jumplink-ref304"></span><a href="javascript:;" reveal-id="ref304" data-open="ref304" class="link link-ref link-reveal xref-bibr">304</a>], but there are various algorithms that work under certain assumptions of the data. One approach to low rank matrix completion is to use the nuclear norm as a convex relaxation of the matrix rank, and use semidefinite programming to find a completion that minimizes the nuclear norm (see [<span class="xrefLink" id="jumplink-ref305"></span><a href="javascript:;" reveal-id="ref305" data-open="ref305" class="link link-ref link-reveal xref-bibr">305</a>, <span class="xrefLink" id="jumplink-ref306"></span><a href="javascript:;" reveal-id="ref306" data-open="ref306" class="link link-ref link-reveal xref-bibr">306</a>]). Although the low-rank matrix completion problem does not depend on any metric, most approaches utilize some kind of metric (such as the nuclear norm, the Euclidean metric or an <span class="inline-formula no-formula-id">|$\ell _p$|</span>-norm). Such approaches may perform well in completion of certain matrix types but do not cover all types of matrices. Moreover, the structure of the data may be more complicated than a matrix with dimension <span class="inline-formula no-formula-id">|$d=2$|⁠</span>. To this end, it is our belief that coupled matrices and tensors are very powerful tools to visualize DT data while maintaining the structural information. For <span class="inline-formula no-formula-id">|$d \geq 3$|⁠</span>, such a dataset is a tensor (a multidimensional array) of order <span class="inline-formula no-formula-id">|$d$|⁠</span>. Tensors are ubiquitous in Big Data. The importance of using tensors in Big Data is illustrated by the fact that they preserve the structure of the data and allow more effective data analysis by incorporating the structure throughout the process. An illustration of coupled matrix–matrix versus coupled tensor–matrix completion is shown in Figure <span class="xrefLink" id="jumplink-f5"></span><a href="javascript:;" data-modal-source-id="f5" class="link xref-fig">5</a>.</p> <h2 scrollto-destination=225532513 id="225532513" class="section-title js-splitscreen-section-title" data-legacy-id=sec5>5 Summary of materials and methodologies</h2> <p class="chapter-para">Table <span class="xrefLink" id="jumplink-TB11"></span><a href="javascript:;" reveal-id="TB11" data-open="TB11" class="link link-reveal link-table xref-fig">11</a> summarizes all the methods we reviewed in this paper along with the databases.</p><div id="box01" class="boxed-text boxed-matter no-caption"><div class=" sec"><div class="title">Key Points</div><ul class="list-simple"><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> <strong>Machine learning:</strong> To our best knowledge, this manuscript is the first which provides a comprehensive list of all the machine learning methods that have been proposed, developed and employed to carry out the task of DTI prediction. A classification of these methods along with advantages and disadvantages of each class of method have been provided.</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> <strong>D</strong>TI software and packages: A list and a short description of all the key software used in DTI predictions is provided. This could help future research, based on their approach to the problem, by helping researchers decide which software and packages suit their problem the best.</p></li><li><p class="chapter-para"><span class="inline-formula no-formula-id">|$\bullet $|</span> <strong>D</strong>TI databases: One of the main challenges in the prediction of DTIs is the fact that not all the interactions between drugs and targets are known. In fact, the number of unknown interactions far exceeds the number of known interactions. As a partial solution, a comprehensive list of all databases along with the most recent update dates and the focus are provided.</p></li></ul></div></div> <h2 scrollto-destination=225532517 id="225532517" class="backacknowledgements-title js-splitscreen-backacknowledgements-title" data-legacy-id=ack1>Funding</h2> <p class="chapter-para">Michigan Lifestage Environmental Exposures and Disease (M-LEEaD) National Institute of Environmental Health Sciences (NIEHS) Core Center (grant P30 ES017885).</p><p class="chapter-para"><strong>Maryam Bagherian</strong> is a postdoctoral research fellow at Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor. Her Ph.D. degree is in applied mathematics and her research includes mathematical physics and mathematical biology.</p><p class="chapter-para"><strong>Elyas Sabeti</strong> is a postdoctoral research fellow at the Michigan Institute for Data Science, University of Michigan, Ann Arbor. He conducts research on data science methodology and its application in healthcare research.</p><p class="chapter-para"><strong>Maureen Sartor</strong> is an associate professor at Department of Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor.</p><p class="chapter-para"><strong>Zaneta Nikolovska-Coleska</strong> is an associate professor at the Department of Pathology, University of Michigan, Ann Arbor.</p><p class="chapter-para"><strong>Kayvan Najarian</strong> is a Professor at Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor. 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