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See what life is like at Optibrium and browse our jobs</div> <div id='header-sub-menu-1705' class='sub-menu-outer'><div class='sub-menu-inner'><ul class='sub-menu'> <li id="menu-item-1835" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-1835"><a href="https://optibrium.com/jobs/">Browse jobs</a></li> </ul></div></div> </li> <li id="menu-item-1711" class="menu-item menu-item-type-post_type menu-item-object-page has-icon menu-item-1711"><div class="menu-item-heading"><a class="menu-item-has-icon" href="https://optibrium.com/about-optibrium/contact-us/">Contact us<span class="menu-item-icon img-fit" aria-hidden="true" style="mask-image: url('https://optibrium.com/wp-content/themes/optibrium/assets/images/theme-icons/email.svg')"></span></a></div><div class="menu-item-content nflm">Our global team is always here to talk and there are a number of ways to get in touch</div></li> </ul></div></div> </li> </ul> <div class="header-search"> <button class="header-search__toggle" type="button" aria-controls="header-search-outer" aria-expanded="false" aria-label="Toggle search"></button> <div id="header-search-outer" class="header-search__outer"> <div class="header-search__inner"> <form role="search" method="get" class="search-form" action="https://optibrium.com/"> <label> <span class="screen-reader-text">Search for:</span> <input type="search" class="search-field" placeholder="Enter a search word..." value="" name="s" /> </label> <input type="submit" class="search-submit" value="Search" /> </form> </div> </div> </div> </div> </div> </nav> </div> </header> <main id="main" class="site-main nflm"> <div class="site-main__inner"> <div class="site-main__content blocks"> <div class="page-header wp-block alignfull has-breadcrumbs is-style-vertical"> <div class="page-header__wrapper"> <div class="page-header__outer"> <nav class="breadcrumbs" aria-label="breadcrumbs"> <span class="breadcrumbs__yoast-wrapper"><span><a href="https://optibrium.com/">Home</a></span> <span class="breadcrumbs__yoast-separator"></span> <span><a href="https://optibrium.com/knowledge-base/">Resources</a></span> <span class="breadcrumbs__yoast-separator"></span> <span class="breadcrumb_last" aria-current="page">What’s the difference between QSAR and imputation predictive models – which method should I use and when?</span></span></nav> <div class="page-header__inner"> <h1 class="page-header__heading">What's the difference between QSAR and imputation predictive models – which method should I use and when?</h1> <ul class="page-header__labels nflm flex-list"> <li> <a href="https://optibrium.com/product/cerella/">Cerella</a> </li> <li> <a href="https://optibrium.com/product/stardrop/">StarDrop</a> </li> <li> <a href="https://optibrium.com/product/adme-qsar/">ADME QSAR</a> </li> <li> <a href="https://optibrium.com/topic/ai-guided-discovery/">AI-guided discovery</a> </li> <li> <a href="https://optibrium.com/topic/exploring-structure-activity-relationships/">Exploring structure-activity relationships</a> </li> <li> <a href="https://optibrium.com/topic/predictive-modelling/">Predictive modelling</a> </li> </ul> <div class="page-header__team nflm"> <p class="page-header__team-heading">Author</p> <ul class="page-header__team__items flex-list"> <li> <span>Matt Segall, PhD</span> </li> </ul> </div> </div> </div> </div> </div> <section class="in-page-menu wp-block alignwide"> <div class="in-page-menu__outer"> <div class="in-page-menu__inner"> <h2 class="in-page-menu__heading is-style-typestyle-h4"> </h2> <div class="in-page-menu__content nflm"> <p>We’re often asked, <em>“What’s the difference between QSAR and imputation models?”</em>, so I’m going to explain how the methods differ, their advantages and disadvantages, and when each approach is applicable.</p> </div> </div> <nav class="in-page-menu__nav nflm"> <h3 class="in-page-menu__nav-heading is-style-typestyle-h6">In this article</h3> <ul> <li class="in-page-menu__item"> <a href="#What-are-QSAR-models?">What are QSAR models?</a> </li> <li class="in-page-menu__item"> <a href="#Difference-between-QSAR-and-imputation-models">Difference between QSAR and imputation models</a> </li> <li class="in-page-menu__item"> <a href="#Why-is-it-relevant-to-drug-discovery">Why is it relevant to drug discovery?</a> </li> <li class="in-page-menu__item"> <a href="#Identifying%20a%20false%20negative">Identifying a false negative</a> </li> </ul> </nav> </div> </section> <div class="media-content wp-block is-media-right alignwide"> <div class="media-content__outer"> <div class="media-content__inner nflm"> <h2 class="media-content__heading is-style-typestyle-h3">What are QSAR models?</h2> <div class="media-content__content nflm"> <p><strong>QSAR</strong> stands for <strong>Q</strong>uantitative, <strong>S</strong>tructure-<strong>A</strong>ctivity <strong>R</strong>elationship; in other words, QSAR models capture how differences between chemical structures relate to differences in their activities or other properties. They work by calculating features of a compound’s structure, or ‘descriptors’.</p> </div> </div> <div class="media-content__media"> <div class="video-item"> <button class="video-item__play-button js-video-item-play"> <span class="visually-hidden"> Play video </span> </button> <div class="video-item__media img-fit"> <img decoding="async" width="1024" height="540" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMjc3IDY3NCIgd2lkdGg9IjEyNzciIGhlaWdodD0iNjc0IiBkYXRhLXU9Imh0dHBzJTNBJTJGJTJGb3B0aWJyaXVtLmNvbSUyRndwLWNvbnRlbnQlMkZ1cGxvYWRzJTJGMjAyNCUyRjEwJTJGU2NyZWVuc2hvdC0yMDI0LTEwLTIyLTE1NTkzMi5wbmciIGRhdGEtdz0iMTI3NyIgZGF0YS1oPSI2NzQiPjwvc3ZnPg==" data-spai="1" class="attachment-large size-large" alt="What's the difference between QSAR and imputation predictive models – which method should I use and when?" sizes="(max-width: 768px) 100vw, 50vw" id="6156" srcset=" " /> </div> <div class="video-item__video"> <div class="video-item__video-inner"> <div class="video-item__video-wrap"> <button class="video-item__video-close cross"> <span class="visually-hidden"> Close Video </span> </button> <iframe title="QSAR vs Imputation" width="640" height="360" data-src="https://www.youtube.com/embed/cZ-Y44b-BQ0?feature=oembed&autoplay=1&modestbranding=1&rel=0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> </div> </div> </div> </div> </div> </div> </div> <figure class="wp-block-image size-full is-resized"><img fetchpriority="high" decoding="async" width="447" height="145" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDcgMTQ1IiB3aWR0aD0iNDQ3IiBoZWlnaHQ9IjE0NSIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmUyLnBuZyIgZGF0YS13PSI0NDciIGRhdGEtaD0iMTQ1Ij48L3N2Zz4=" data-spai="1" alt="From a molecular structure, molecular descriptors can be generated." class="wp-image-6000" style="width:450px" /></figure> <p>QSAR models are built using statistical or machine learning methods to find correlations between the descriptors of many compounds and their experimentally measured values of a property.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="622" height="532" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA2MjIgNTMyIiB3aWR0aD0iNjIyIiBoZWlnaHQ9IjUzMiIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmUzLnBuZyIgZGF0YS13PSI2MjIiIGRhdGEtaD0iNTMyIj48L3N2Zz4=" data-spai="1" alt="QSAR models detect the correlations between sets of descriptors and experimentally-measured property values" class="wp-image-6002" style="width:450px" /></figure> <p>Once built, a QSAR model only needs the chemical structure of a new compound to predict its properties.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="853" height="146" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA4NTMgMTQ2IiB3aWR0aD0iODUzIiBoZWlnaHQ9IjE0NiIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmU1LnBuZyIgZGF0YS13PSI4NTMiIGRhdGEtaD0iMTQ2Ij48L3N2Zz4=" data-spai="1" alt="QSAR models only take into account molecular descriptors to make predictions on properties." class="wp-image-6006" style="width:600px" /></figure> <p>This is ideal for assessing many new compound designs to choose the best compounds to synthesise and test experimentally, for example, for virtual screening or in combination with generative chemistry methods.</p> <h2 class="wp-block-heading" id="Difference-between-QSAR-and-imputation-models">Imputation models are quite different from QSAR</h2> <p>They use both a chemical structures and experimental data as inputs. They learn from correlations between compound descriptors and measured data and between measurements of different experimental endpoints. This approach can gain much more information than QSAR, even when data for many compounds and endpoints are missing; in other words, it can use sparse data.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="876" height="490" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA4NzYgNDkwIiB3aWR0aD0iODc2IiBoZWlnaHQ9IjQ5MCIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmU2LnBuZyIgZGF0YS13PSI4NzYiIGRhdGEtaD0iNDkwIj48L3N2Zz4=" data-spai="1" alt="Imputation models take into account both descriptors and existing experimental data when evaluating correlations to experimental endpoints" class="wp-image-6008" style="width:600px" /></figure> <p>To make predictions using an imputation model, we can input a compound’s structure and any data we have already measured to predict the results of experiments we have not yet run on that compound, thereby <em>imputing</em>, or filling in, the missing data.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="944" height="126" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA5NDQgMTI2IiB3aWR0aD0iOTQ0IiBoZWlnaHQ9IjEyNiIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmU3LnBuZyIgZGF0YS13PSI5NDQiIGRhdGEtaD0iMTI2Ij48L3N2Zz4=" data-spai="1" alt="The process of imputation" class="wp-image-6009" style="width:600px" /></figure> <p>And imputation can be applied to an entire matrix of compounds and data.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="926" height="491" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA5MjYgNDkxIiB3aWR0aD0iOTI2IiBoZWlnaHQ9IjQ5MSIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmU4LnBuZyIgZGF0YS13PSI5MjYiIGRhdGEtaD0iNDkxIj48L3N2Zz4=" data-spai="1" alt="Imputation can deal with large, sparse data sets." class="wp-image-6010" style="width:600px" /></figure> <h2 class="wp-block-heading" id="Why-is-it-relevant-to-drug-discovery">So, why is this relevant to drug discovery?</h2> <p>The data we have in drug discovery is very sparse. We typically have plenty of data for early, high-throughput, low-cost experiments, such as <em>in vitro</em> biochemical activities or simple physicochemical properties. But, as we progress downstream to more expensive, lower throughput experiments, such as phenotypic screens or in vivo studies, we have less and less data.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="996" height="546" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA5OTYgNTQ2IiB3aWR0aD0iOTk2IiBoZWlnaHQ9IjU0NiIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmU5LnBuZyIgZGF0YS13PSI5OTYiIGRhdGEtaD0iNTQ2Ij48L3N2Zz4=" data-spai="1" alt="Pharma data is often sparse, particularly for later-stage experimental endpoints which are more expensive to measure" class="wp-image-6011" style="width:600px" /></figure> <p>Of course, these later-stage experiments are the most valuable for selecting the best candidate drugs. But, these outcomes are impossible to predict using QSAR models because they are very complex, often being determined by multiple factors, including target activities, ADME and physicochemical properties, meaning that an enormous amount of data would be required to dissect these complex relationships with chemical structures. And, of course, these are the experiments for which we have the <em>least </em>data!</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="1011" height="546" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMDExIDU0NiIgd2lkdGg9IjEwMTEiIGhlaWdodD0iNTQ2IiBkYXRhLXU9Imh0dHBzJTNBJTJGJTJGb3B0aWJyaXVtLmNvbSUyRndwLWNvbnRlbnQlMkZ1cGxvYWRzJTJGMjAyNCUyRjEwJTJGUGljdHVyZTEwLnBuZyIgZGF0YS13PSIxMDExIiBkYXRhLWg9IjU0NiI+PC9zdmc+" data-spai="1" alt="QSAR models are not as good as imputation models for late-stage predictions" class="wp-image-6012" style="width:600px" /></figure> <p>However, the data from the earlier experiments contain very valuable information with which to predict downstream experimental outcomes, and imputation methods can leverage these data to make much more accurate predictions than QSAR models by filling in the missing values.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" width="996" height="547" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA5OTYgNTQ3IiB3aWR0aD0iOTk2IiBoZWlnaHQ9IjU0NyIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRlBpY3R1cmUxMS5wbmciIGRhdGEtdz0iOTk2IiBkYXRhLWg9IjU0NyI+PC9zdmc+" data-spai="1" alt="Imputation provides new insight into complex data" class="wp-image-6013" style="width:600px" /></figure> <p>That, in turn, enables us to make much more confident predictions of the best compounds to progress, saving time and money while improving the chances of delivering a high-quality candidate.</p> <h2 class="wp-block-heading" id="Identifying-a-false-negative">And it’s not only about making more accurate predictions.</h2> <p>Building an imputation model captures information about the relationships between different experimental endpoints, giving us additional valuable insights.</p> <p>The relationships between experimental endpoints, revealed from the complex, sparse data by an imputation model, can help to identify mechanisms of action and adverse outcome pathways.</p> <p>The model can also use this information to suggest the next experiments to perform that will generate the most valuable data to make even more accurate predictions. This ‘active learning’ approach maximises the return on investment for your experimental resources.</p> <figure class="wp-block-image size-large is-resized"><img decoding="async" width="1024" height="534" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMDQ4IDU0NyIgd2lkdGg9IjEwNDgiIGhlaWdodD0iNTQ3IiBkYXRhLXU9Imh0dHBzJTNBJTJGJTJGb3B0aWJyaXVtLmNvbSUyRndwLWNvbnRlbnQlMkZ1cGxvYWRzJTJGMjAyNCUyRjEwJTJGUGljdHVyZTEyLnBuZyIgZGF0YS13PSIxMDQ4IiBkYXRhLWg9IjU0NyI+PC9zdmc+" data-spai="1" alt="Imputation can tell you which endpoints should be prioritised for experimental measurement, guiding design" class="wp-image-6014" style="width:600px" srcset=" " sizes="(max-width: 1024px) 100vw, 1024px" /></figure> <p>And, the model can identify measured values that are highly unlikely given all of the other data in your data set. These outliers reveal valuable new opportunities that you may have missed due to missing, uncertain or incorrect data, such as false negatives.</p> <p>So, imputation models are most applicable when you have a small amount of experimental data for your compounds and want to use these to make the best decisions to progress from concept to candidate faster and cheaper.</p> <section class="wysiwyg wp-block alignwide has-text-align-center"> <div class="wysiwyg__inner nflm"> <h2 class="wysiwyg__heading is-style-typestyle-h3">Interested in building your own imputation and QSAR models?</h2> <div class="wysiwyg__content nflm"> <p><a href="https://optibrium.com/products/cerella/">Cerella</a> automatically builds and updates both imputation and QSAR models, so you can benefit from both methods and ensure that you make decisions based on the latest information.</p> <p><a class="g-button is-yellow has-arrow" href="https://optibrium.com/products/cerella/">Discover Cerella</a></p> </div> </div> </section> <section class="cards wp-block alignfull"> <div class="cards__outer"> <div class="cards__inner"> <div class="cards__header"> <h2 aria-label="More QSAR and imputation models resources" class="cards__heading is-style-typestyle-h1 has-tags">More QSAR and imputation models <span>resources</span></h2> </div> <div class="cards__items"> <article class="g-card is-knowledge-base is-ebooks has-image has-link"> <div class="g-card__outer"> <div class="g-card__inner"> <h2 class="g-card__heading"><a href="https://optibrium.com/knowledge-base/cerella-case-studies-ebook/">Cerella case studies ebook</a> </h2> <div class="g-card__content"> <p>In this ebook we demonstrate our deployable AI discovery platform, Cerella™. Browse real-world stories of success from our collaborations with AstraZeneca, Genetech, Takeda Pharmaceuticals, Constellation Pharmaceuticals and many more.</p> </div> </div> <div class="g-card__footer"> <div class="g-card__labels"> <div class="g-card__labels__items"> <a href="https://optibrium.com/topic/ai-guided-discovery/" class="g-button is-small">AI-guided discovery</a> </div> </div> <div class="g-card__meta"> 21 June 2024 </div> </div> </div> <div class="g-card__media img-fit"> <img decoding="async" width="768" height="512" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMjAwIDgwMCIgd2lkdGg9IjEyMDAiIGhlaWdodD0iODAwIiBkYXRhLXU9Imh0dHBzJTNBJTJGJTJGb3B0aWJyaXVtLmNvbSUyRndwLWNvbnRlbnQlMkZ1cGxvYWRzJTJGMjAyNCUyRjA2JTJGV2Vic2l0ZS1iYW5uZXIuanBnIiBkYXRhLXc9IjEyMDAiIGRhdGEtaD0iODAwIj48L3N2Zz4=" data-spai="1" class="attachment-medium_large size-medium_large" alt="" id="5269" srcset=" " sizes="(max-width: 768px) 100vw, 768px" /> </div> </article> <article class="g-card is-knowledge-base is-publication has-image has-link"> <div class="g-card__outer"> <div class="g-card__inner"> <h2 class="g-card__heading"><a href="https://optibrium.com/knowledge-base/imputation-of-assay-bioactivity-data-using-deep-learning/">Imputation of assay bioactivity data using deep learning</a> </h2> <div class="g-card__content"> <p>This article describes a novel deep learning neural network method and its application for the imputation of bioactivity data, such…</p> </div> </div> <div class="g-card__footer"> <div class="g-card__labels"> <div class="g-card__labels__items"> <a href="https://optibrium.com/topic/ai-guided-discovery/" class="g-button is-small">AI-guided discovery</a> </div> </div> <div class="g-card__meta"> 14 February 2019 </div> </div> </div> <div class="g-card__media img-fit"> <img decoding="async" width="500" height="179" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA1MDAgMTc5IiB3aWR0aD0iNTAwIiBoZWlnaHQ9IjE3OSIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMTklMkYwMiUyRkltcHV0YXRpb24tb2YtYmlvYWN0aXZpdHktZGF0YV9HQS5naWYuanBnIiBkYXRhLXc9IjUwMCIgZGF0YS1oPSIxNzkiPjwvc3ZnPg==" data-spai="1" class="attachment-medium_large size-medium_large" alt="" id="1116" /> </div> </article> <article class="g-card is-webinar on-demand has-image has-link"> <div class="g-card__outer"> <div class="g-card__inner"> <span class="g-card__top-meta"> On-demand </span> <h2 class="g-card__heading"><a href="https://optibrium.com/webinars/pka-prediction-using-quantum-mechanics-and-machine-learning/">pKa prediction using quantum mechanics and machine learning</a> </h2> <div class="g-card__content"> <p>The dissociation of a proton from a heteroatom has a significant influence on the charge distribution and interactions of a…</p> </div> </div> <div class="g-card__footer"> <div class="g-card__labels"> <div class="g-card__labels__items"> <a href="https://optibrium.com/topic/other-adme-and-physicochemical-properties/" class="g-button is-small">Other ADME and physicochemical properties</a> </div> </div> <div class="g-card__meta"> 30 October 2019 </div> </div> </div> <div class="g-card__media img-fit"> <img decoding="async" width="768" height="432" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxOTIwIDEwODAiIHdpZHRoPSIxOTIwIiBoZWlnaHQ9IjEwODAiIGRhdGEtdT0iaHR0cHMlM0ElMkYlMkZvcHRpYnJpdW0uY29tJTJGd3AtY29udGVudCUyRnVwbG9hZHMlMkYyMDI0JTJGMDYlMkZwS2EtcHJlZGljdGlvbi11c2luZy1xdWFudHVtLW1lY2hhbmljcy1hbmQtbWFjaGluZS1sZWFybmluZy0ucG5nIiBkYXRhLXc9IjE5MjAiIGRhdGEtaD0iMTA4MCI+PC9zdmc+" data-spai="1" class="attachment-medium_large size-medium_large" alt="" id="5314" srcset=" " sizes="(max-width: 768px) 100vw, 768px" /> </div> </article> </div> </div> </div> </section> </div> </div> </main> 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