Ardigen phenAID’s Multimodal Approach Improves MoA and Bioactivity Prediction When Applied to a HCS Dataset from a Big Pharma Company

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href="http://twitter.com/share?url=https://ardigen.com/multimodal-approach-improves-mode-of-action-and-bioactivity-prediction/&text=Ardigen%20phenAID%E2%80%99s%20Multimodal%20Approach%20Improves%20MoA%20and%20Bioactivity%20Prediction%20When%20Applied%20to%20a%20HCS%20Dataset%20from%20a%20Big%20Pharma%20Company%20" target="_blank" ><img src="https://ardigen.com/wp-content/plugins/simple-share-buttons-adder/buttons/somacro/twitter.png" style="width: 35px;" title="Twitter" class="ssba ssba-img" alt="Tweet about this on Twitter" /><div title="Twitter" class="ssbp-text">Twitter</div></a><a data-site="linkedin" class="ssba_linkedin_share ssba_share_link" href="http://www.linkedin.com/shareArticle?mini=true&url=https://ardigen.com/multimodal-approach-improves-mode-of-action-and-bioactivity-prediction/" target="_blank" ><img src="https://ardigen.com/wp-content/plugins/simple-share-buttons-adder/buttons/somacro/linkedin.png" style="width: 35px;" title="LinkedIn" class="ssba ssba-img" alt="Share on LinkedIn" /><div title="Linkedin" class="ssbp-text">Linkedin</div></a></div></div> </div> <div class="container"> <div class="intro-flex"> <div class="intro-flex-item"> <div class="intro-item"> <div class="intro-top"> <div class="intro-date" uk-scrollspy="cls:uk-animation-slide-left-medium"> <span>24</span> <span>July 2024</span> </div> <div class="intro-line"> <div class="intro-author" uk-scrollspy="cls:uk-animation-slide-left-medium"> Maurycy Chronowski </div> </div> </div> <div class="intro-author-mobile" uk-scrollspy="cls:uk-animation-slide-left-medium"> Maurycy Chronowski </div> <div class="intro-content" uk-scrollspy="cls:uk-animation-slide-right-medium"> <div class="intro-title"> <h1>Ardigen phenAID’s Multimodal Approach Improves MoA and Bioactivity Prediction When Applied to a HCS Dataset from a Big Pharma Company</h1> </div> <div class="intro-short"> <p></p> </div> </div> </div> </div> </div> </div> </section> <section class="post tc" uk-scrollspy="cls:uk-animation-slide-bottom-medium"> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <p><span style="font-weight: 400;">High-content screening (HSC) assays such as Cell Painting represent a rich data source for phenotypic drug discovery. Machine learning (ML) and Artificial Intelligence (AI) methods for image-based profiling are enabling a new era in phenotypic drug discovery. In particular, introducing ML into your workflows enhances the quality of prediction through </span><b>multimodal approaches</b><span style="font-weight: 400;">. You can read more about this in our previous article: </span><a href="https://ardigen.com/high-content-screening-redefining-what-is-possible-with-artificial-intelligence-and-machine-learning/">High Content Screening: Redefining What Is Possible with Artificial Intelligence and Machine Learning.</a></p> <p><span style="font-weight: 400;">Human-defined morphological features, such as size, shape, intensity, granularity, and texture, can be extracted from HCS data by using image analysis tools such as CellProfiler</span><span style="font-weight: 400;">1</span><span style="font-weight: 400;">. However, this can take hundreds of hours and may not be able to extract all the insights packed in the images. ML methods not only automate image analysis but also enable feature extraction that extends beyond human-defined morphological features. </span></p> <p><span style="font-weight: 400;">In addition to the phenotypic features, chemical features of the tested compounds also provide a valuable source of data for predicting potential drug candidates. To empower researchers and accelerate phenotypic drug discovery, we have developed the Ardigen phenAID platform, a solution that introduces </span><b>multimodal predictive capabilities</b><span style="font-weight: 400;"> to HSC image analysis. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><span style="font-weight: 400;">Analyzing phenotypic and chemical features with Ardigen phenAID</span></h2> <p><a href="https://ardigen.com/biomedical-imaging/"><span style="font-weight: 400;">Ardigen phenAID </span></a><span style="font-weight: 400;">is an AI-powered platform that combines multiple modalities, such as image and chemical structure data, to enhance the quality of predictions. PhenAID enables automated extraction of diverse morphological features, together with the classification and clustering of cellular phenotypes to help identify potential drug candidates. In addition to extracting phenotypic insights from HCS data, phenAID incorporates information about the chemical structure of the screened compounds. </span></p> <p><span style="font-weight: 400;">This multimodal approach provides powerful insights into the Mode of Action (MoA) and bioactivity property predictions. To show its effectiveness, we benchmarked this method using a validated, proprietary dataset from Merck containing thousands of compounds. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2></h2> <h2><span style="font-weight: 400;">Ardigen phenAID platform applied to a proprietary dataset from Merck KGaA</span></h2> <p><span style="font-weight: 400;">In a pilot project with Merck, we applied the Ardigen phenAID platform to analyze one of their proprietary HSC datasets. The analysis consisted of two tasks: mode of action and bioactivity properties prediction. For each task, we assessed performance using one modality (image or chemical structures features) or multimodal approach (image and chemical structure features) with both human-defined and AI-based features.</span></p> <p><span style="font-weight: 400;">For each input modality, we trained two models: one for MoA and one for bioactivity properties prediction. We compared AI-based features (extracted with image and molecule representation models) with human-defined descriptors (CellProfiler and ECFP for images and structures respectively).</span> <span style="font-weight: 400;">For both methods, we assessed the confidence of the predictive scores when using only one modality versus multimodal approaches. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <img src="https://ardigen.com/wp-content/uploads/2024/07/Untitled-presentation-18.png" alt="" style="float:left" class="float-left"> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Figure 1.</strong> Method overview: Images and structures are processed using either AI model or human-defined features and passed through MLP to obtain predictions.</p> <h2></h2> <h2><span style="font-weight: 400;">Multimodal approach boosts the accuracy of prediction</span></h2> <p><span style="font-weight: 400;">The multimodal approach consistently improved the prediction scores across all tested setups. It enhanced both the Mode of Action and bioactivity property prediction on a diverse set of biological tasks (</span><b>Table 1</b><span style="font-weight: 400;">).</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <img src="https://ardigen.com/wp-content/uploads/2024/07/Untitled-presentation-17.png" alt="" style="float:left" class="float-left"> <p> </p> <p>Table 1<span style="font-weight: 400;">: A</span> <span style="font-weight: 400;">numerical comparison between modalities using ROC AUC scores. For </span><b>AI-driven modalities</b><span style="font-weight: 400;">, phenotypic representations were generated using a Deep Convolutional Neural Network (GapNet</span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916854/"><span style="font-weight: 400;">2</span></a><span style="font-weight: 400;">) pre-trained using a compound-matching task (DL) and representations of chemical structures generated using a proprietary Relative Molecule Attention Transformer (RMAT</span><a href="https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00789-7"><span style="font-weight: 400;">3</span></a><span style="font-weight: 400;">) model.</span> <b>Human-defined modalities</b><span style="font-weight: 400;"> were obtained using CellProfiler (CP</span><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04344-9"><span style="font-weight: 400;">1</span></a><span style="font-weight: 400;">) for morphological features and Extended-Connectivity Fingerprints (ECFP</span><a href="https://pubs.acs.org/doi/10.1021/ci100050t"><span style="font-weight: 400;">4</span></a><span style="font-weight: 400;">) for chemical structures. Bold values represent the best result. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <p><span style="font-weight: 400;">Incorporating machine learning and AI solutions for extracting phenotypic features streamlines drug discovery efforts, reducing both the cost and time of analysis. Using deep learning and AI models, we could extract features faster and cheaper while retaining the prediction performance. </span></p> <p><span style="font-weight: 400;">Through this study, we have demonstrated that multimodal approaches increase the confidence of predictions when it comes to HSC data analysis. By combining phenotypic features extracted from the images with chemical structures of the active molecules, we were able to enhance Mode of Action and bioactivity prediction for dozens of compounds tested by Merck KGaA.</span></p> <p><span style="font-weight: 400;">To learn more about the results of this study, </span><a href="https://app-eu1.hubspot.com/documents/25781697/view/823007909?accessId=769987"><span style="font-weight: 400;">download our poster</span></a><span style="font-weight: 400;">. </span></p> <p>You can learn more about the phenAID platform <a href="https://ai.ardigen.com/phenaid">here</a>.</p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <p> </p> <h2><b>Works Cited:</b></h2> <ol> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Stirling, D.R., et al. (2021). “CellProfiler 4: improvements in speed, utility and usability”, </span><i><span style="font-weight: 400;">BMC Bioinformatics</span></i><span style="font-weight: 400;"> 22, 433. DOI: 10.1186/s12859-021-04344-9. </span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Rumetshofer, E., et al. (2018). “Human-level protein localization with convolutional neural networks,” </span><i><span style="font-weight: 400;">International conference on learning representations</span></i><span style="font-weight: 400;">.</span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Maziarka, Ł. et al. (2024). “Relative molecule self-attention transformer,” </span><i><span style="font-weight: 400;">J Cheminform</span></i><span style="font-weight: 400;"> 16, 3. DOI: 10.1186/s13321-023-00789-7</span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Rogers, D., and Hahn, M. (2010). “Extended-Connectivity Fingerprints”, </span><i><span style="font-weight: 400;">J. Chem. Inf. Model. </span></i><span style="font-weight: 400;">50, 5, 742–754 DOI: 10.1021/ci100050t</span></li> </ol> <p> </p> </div> </div> </section> <section class="latest" style="background-image: url('https://ardigen.com/wp-content/uploads/2018/04/img_bg_call_to.png')"> <div class="container"> <div class="mirror-flex mirror"> <div class="mirror-flex-item"> <div class="mirror-item" uk-scrollspy="cls:uk-animation-slide-left-medium"> <div class="mirror-image" style="background-image: url('https://ardigen.com/wp-content/uploads/2024/07/posty-na-linked-in-900-x-500-px-5.png')"> <div class="mirror-dim"></div> <div class="mirror-mask"></div> <div class="mirror-date">16 July 2024</div> </div> <div class="mirror-content"> <div class="mirror-title"> <h6>High Content Screening: Redefining What Is Possible with Artificial Intelligence and Machine Learning</h6> </div> <div class="mirror-btn"> <a href="https://ardigen.com/?p=16633">Previous <img 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Ardigen phenAID’s Multimodal Approach Improves MoA and Bioactivity Prediction When Applied to a HCS Dataset from a Big Pharma Company – Ardigen.com