Cerella: Proven AI-guided drug discovery

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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-backdrop has-backdrop-cerella has-breadcrumbs is-style-small-media has-media"> <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/products/">Products</a></span> <span class="breadcrumbs__yoast-separator"></span> <span class="breadcrumb_last" aria-current="page">Cerella</span></span></nav> <div class="page-header__inner"> <h1 aria-label="Go from drug concept to candidate, faster" class="page-header__heading has-tags">Go from drug concept to candidate, <span>faster</span></h1> </div> <div class="page-header__media"> <img fetchpriority="high" decoding="async" width="7492" height="1571" src="https://optibrium.com/wp-content/uploads/2024/05/Cerella-logo.svg" class="attachment-medium_large size-medium_large" alt="Cerella logo" loading="eager" id="4290" /> </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">Proven AI-powered drug discovery</h2> <div class="in-page-menu__content nflm"> <p>Cerella creates new value from your drug discovery data, revealing hidden insights into the best compounds and most valuable experiments for your project.</p> <p>It makes confident predictions, accurately filling in missing values, especially for expensive downstream experiments that can’t be predicted by other methods. This enables you to do more, even with sparse, limited data sets.</p> </div> </div> <nav class="in-page-menu__nav nflm"> <h3 class="in-page-menu__nav-heading is-style-typestyle-h6">On this page</h3> <ul> <li class="in-page-menu__item"> <a href="#benefits">Benefits</a> </li> <li class="in-page-menu__item"> <a href="#case-study">Case studies</a> </li> <li class="in-page-menu__item"> <a href="#resources">Resources</a> </li> </ul> </nav> </div> </section> <div class="media-content wp-block is-media-left alignwide"> <div class="media-content__outer"> <div class="media-content__inner nflm"> <h2 aria-label="Drug discovery data is complex" class="media-content__heading is-style-typestyle-h3 has-tags">Drug discovery data <span>is complex</span></h2> <div class="media-content__content nflm"> <p>With endless possible chemical space to explore and experiments to perform, how do you choose the best compounds and <strong>prioritise your resources?</strong></p> <p>Add to this the inevitability of errors and uncertainty, and you get your next challenge – <strong>how can you trust the data</strong> which you do have?</p> <p>You know how important it is to <strong>find high-quality candidate drugs, fas</strong>t. A streamlined workflow is essential, but complex structure-activity relationships, hidden mechanisms of action and adverse outcome pathways make it tricky to see which compounds to progress and the experiments that can be cut.</p> <p>Want to gain a <strong>competitive advantage</strong>?</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="535" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMjc4IDY2OCIgd2lkdGg9IjEyNzgiIGhlaWdodD0iNjY4IiBkYXRhLXU9Imh0dHBzJTNBJTJGJTJGb3B0aWJyaXVtLmNvbSUyRndwLWNvbnRlbnQlMkZ1cGxvYWRzJTJGMjAyNCUyRjA1JTJGVHJhbnNmb3JtYXRpdmUtaW5zaWdodHMtcmV2ZWFsZWQtYnktQ2VyZWxsYS1Zb3VUdWJlLnBuZyIgZGF0YS13PSIxMjc4IiBkYXRhLWg9IjY2OCI+PC9zdmc+" data-spai="1" class="attachment-large size-large" alt="AI-guided drug discovery: Cerella video screenshot" sizes="(max-width: 768px) 100vw, 50vw" id="1147" 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="Transformative insights, revealed by Cerella" width="640" height="360" data-src="https://www.youtube.com/embed/VWYD0gZQ8J8?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> <section id="benefits" class="stacker wp-block alignfull"> <div class="stacker__outer"> <div class="stacker__inner"> <div class="stacker__header nflm"> <h2 aria-label="Transformative insights, revealed" class="stacker__heading is-style-typestyle-h2 has-tags">Transformative insights, <span>revealed</span></h2> <div class="statcker__content nflm"> <p>Meet Cerella, our deployable AI which speeds up your projects, guides your decisions, and pinpoints the best compounds for your objectives.</p> </div> </div> <a href="#contact-form" class="g-button is-yellow stacker__cta">Discover Cerella</a> <div class="stacker__items"> <div class="stacker__item"> <div class="stacker__item-outer has-blue3-background-color"> <div class="stacker__item-inner"> <h3 class="stacker__item-heading is-style-typestyle-h4 has-tags"> Use early-stage data to predict late-stage outcomes, avoiding unnecessary and expensive measurements </h3> </div> </div> </div> <div class="stacker__item"> <div class="stacker__item-outer has-orange-background-color"> <div class="stacker__item-inner"> <h3 class="stacker__item-heading is-style-typestyle-h4 has-tags"> Uncover hidden opportunities caused by missing, uncertain or inaccurate data </h3> </div> </div> </div> <div class="stacker__item"> <div class="stacker__item-outer has-green-background-color"> <div class="stacker__item-inner"> <h3 class="stacker__item-heading is-style-typestyle-h4 has-tags"> Create value from all your data, informing future strategies by harnessing existing data from parallel or previous projects </h3> </div> </div> </div> <div class="stacker__item"> <div class="stacker__item-outer has-blue4-background-color"> <div class="stacker__item-inner"> <h3 class="stacker__item-heading is-style-typestyle-h4 has-tags"> Identify the most valuable measurements which will maximise the return on investment of your experimental efforts </h3> </div> </div> </div> <div class="stacker__item"> <div class="stacker__item-outer has-orange-background-color"> <div class="stacker__item-inner"> <h3 class="stacker__item-heading is-style-typestyle-h4 has-tags"> Target high-quality compounds with confidence, using robust uncertainty estimates </h3> </div> </div> </div> </div> </div> </div> </section> <blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow"> <p>By integrating machine learning and AI into our R&D efforts we expect to identify promising compounds and rapidly optimize their properties which will accelerate our pipeline. We aim to revolutionize how we develop sustainable solutions for farmers enhance global food security and trust that Optibrium’s software solutions will get us there.</p> <cite>Dr. Seva Rostovtsev, Executive VP and CTO, FMC</cite></blockquote> <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 trying Cerella?</h2> <div class="wysiwyg__content nflm"> <p>Get in touch to chat with us about how Cerella could improve your discovery workflows</p> <p><a class="g-button is-yellow arrow-link" href="#contact-form">Get in touch</a></p> </div> </div> </section> <section id="case-study" class="tabs wp-block alignfull"> <div class="tabs__outer"> <div class="tabs__inner"> <h2 aria-label="Proven success in drug discovery" class="tabs__heading is-style-typestyle-h1 has-tags"><span>Proven success</span> in drug discovery</h2> <div class="tabs__tabs"> <div class="tabs__controls automatic" role="tablist" aria-labelledby="tablist-1"> <button type="button" role="tab" id="tab-0" aria-selected="true" aria-controls="tabpanel-0" >AstraZeneca </button> <button type="button" role="tab" id="tab-1" aria-selected="false" aria-controls="tabpanel-1" tabindex="-1" >Constellation Pharmaceuticals </button> <button type="button" role="tab" id="tab-2" aria-selected="false" aria-controls="tabpanel-2" tabindex="-1" >FMC </button> <button type="button" role="tab" id="tab-3" aria-selected="false" aria-controls="tabpanel-3" tabindex="-1" >IFF </button> <button type="button" role="tab" id="tab-4" aria-selected="false" aria-controls="tabpanel-4" tabindex="-1" >Genentech </button> <button type="button" role="tab" id="tab-5" aria-selected="false" aria-controls="tabpanel-5" tabindex="-1" >Open Source Malaria </button> <button type="button" role="tab" id="tab-6" aria-selected="false" aria-controls="tabpanel-6" tabindex="-1" >Takeda Pharmaceuticals </button> <button type="button" role="tab" id="tab-7" aria-selected="false" aria-controls="tabpanel-7" tabindex="-1" >Undisclosed collaborator </button> </div> <div class="tabs__panels"> <div role="tabpanel" tabindex="0" id="tabpanel-0" aria-labelledby="tab-0" class="tabs__panel nflm "> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">See how Cerella translated in vitro data to in vivo savings for AstraZeneca.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; 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autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in <a href="https://doi.org/10.1021/acs.molpharmaceut.2c00027" target="_blank" rel="noopener"><em>Molecular Pharmaceutics</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/predicting-pharmacokinetic-parameters-and-curves/">Watch the webinar</a></p> </div> </div> </div> <div role="tabpanel" tabindex="0" id="tabpanel-1" aria-labelledby="tab-1" class="tabs__panel nflm is-hidden"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Hear how Constellation Pharmaceuticals saved “two months’ worth of 12 chemists’ workloads” by identifying inactive compounds.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; 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autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in the <a href="https://doi.org/10.1021/acs.jcim.0c00443" target="_blank" rel="noopener"><em>Journal of Chemical Information and Modeling</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/knowledge-base/practical-applications-of-deep-learning-to-impute-heterogeneous-drug-discovery-data/">Watch the webinar</a></p> </div> </div> </div> <div role="tabpanel" tabindex="0" id="tabpanel-2" aria-labelledby="tab-2" class="tabs__panel nflm is-hidden"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Discover how FMC used Cerella to guide purchasing and screening decisions. </h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/jv0Zt8X_yTU?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/jv0Zt8X_yTU/maxresdefault.jpg"></a>" title="The power of AI applied to agrochemical bioactivity" width="500" height="281" src="https://www.youtube-nocookie.com/embed/jv0Zt8X_yTU?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/the-power-of-ai-applied-to-agrochemical-bioactivity/">Watch the webinar</a></p> </div> </div> </div> <div role="tabpanel" tabindex="0" id="tabpanel-3" aria-labelledby="tab-3" class="tabs__panel nflm is-hidden"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Hear how Cerella predicted complex in vivo sensory responses, accurately, for International Flavors & Fragrances. </h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/t6FR-EgEs9I?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/t6FR-EgEs9I/maxresdefault.jpg"></a>" title="Imputation of sensory properties using deep learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/t6FR-EgEs9I?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in the <a href="https://doi.org/10.1007/s10822-021-00424-3" target="_blank" rel="noopener"><em>Journal of Computer-Aided Molecular Design</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/imputation-of-sensory-properties-using-deep-learning-webinar/">Watch the webinar</a></p> </div> </div> </div> <div role="tabpanel" tabindex="0" id="tabpanel-4" aria-labelledby="tab-4" class="tabs__panel nflm is-hidden"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">A 76% reduction in the number of assays required. See how Cerella could transform Genentech’s kinase programmes.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/D7ReuGUpczI?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/D7ReuGUpczI/maxresdefault.jpg"></a>" title="Optimising kinase profiling programmes with deep learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/D7ReuGUpczI?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/optimising-kinase-profiling-programmes-with-deep-learning/">Watch the webinar</a></p> </div> </div> </div> <div role="tabpanel" tabindex="0" id="tabpanel-5" aria-labelledby="tab-5" class="tabs__panel nflm is-hidden"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Learn how Cerella was able to find the only compound experimentally proven as active in the OSM competition.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/s0t6wsIy5KM?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/s0t6wsIy5KM/maxresdefault.jpg"></a>" title="AI-guided design of antimalarials with in vitro validation" width="500" height="281" src="https://www.youtube-nocookie.com/embed/s0t6wsIy5KM?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in the<a href="https://doi.org/10.1021/acs.jmedchem.1c00313" target="_blank" rel="noopener"> <em>Journal of Medicinal Chemistry</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/ai-guided-design-of-antimalarials-with-in-vitro-validation/">Watch the webinar</a></p> </div> </div> </div> <div role="tabpanel" tabindex="0" id="tabpanel-6" aria-labelledby="tab-6" class="tabs__panel nflm is-hidden"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Discover how Cerella handled 700,000 compounds and 1,000 experimental endpoints for Takeda Pharmaceuticals.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/Ks7supfZ6oQ?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/Ks7supfZ6oQ/maxresdefault.jpg"></a>" title="Large Scale Imputation of Drug Discovery Data using Deep Learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/Ks7supfZ6oQ?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in<a href="https://optibrium.com/knowledge-base/deep-imputation-on-large-scale-drug-discovery-data/"> <em>Applied AI Letters</em>.</a></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/large-scale-imputation-of-drug-discovery-data-using-deep-learning/">Watch the webinar</a></p> </div> </div> </div> <div role="tabpanel" tabindex="0" id="tabpanel-7" aria-labelledby="tab-7" class="tabs__panel nflm is-hidden"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">See how Cerella helped obtain accurate <span>in vivo</span> PK profiles from limited <span>in vitro</span> ADME data for our collaborator.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/pUpZf-4w8gg?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/pUpZf-4w8gg/maxresdefault.jpg"></a>" title="Predicting pharmacokinetics from limited ADME data with deep learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/pUpZf-4w8gg?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/real-world-case-studies-predicting-pharmacokinetics-from-limited-adme-data-with-deep-learning/">Watch the webinar</a></p> </div> </div> </div> </div> </div> <div class="tabs__accordion"> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">AstraZeneca</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">See how Cerella translated in vitro data to in vivo savings for AstraZeneca.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/e5vcwD2N3Y8?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/e5vcwD2N3Y8/maxresdefault.jpg"></a>" title="Predicting PK parameters and curves" width="500" height="281" src="https://www.youtube-nocookie.com/embed/e5vcwD2N3Y8?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in <a href="https://doi.org/10.1021/acs.molpharmaceut.2c00027" target="_blank" rel="noopener"><em>Molecular Pharmaceutics</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/predicting-pharmacokinetic-parameters-and-curves/">Watch the webinar</a></p> </div> </div> </div> </details> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">Constellation Pharmaceuticals</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Hear how Constellation Pharmaceuticals saved “two months’ worth of 12 chemists’ workloads” by identifying inactive compounds.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/-jYOnqYoP8Q?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/-jYOnqYoP8Q/maxresdefault.jpg"></a>" title="Practical Applications of Deep Learning to Imputation of Drug Discovery Data" width="500" height="281" src="https://www.youtube-nocookie.com/embed/-jYOnqYoP8Q?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in the <a href="https://doi.org/10.1021/acs.jcim.0c00443" target="_blank" rel="noopener"><em>Journal of Chemical Information and Modeling</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/knowledge-base/practical-applications-of-deep-learning-to-impute-heterogeneous-drug-discovery-data/">Watch the webinar</a></p> </div> </div> </div> </details> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">FMC</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Discover how FMC used Cerella to guide purchasing and screening decisions. </h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/jv0Zt8X_yTU?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/jv0Zt8X_yTU/maxresdefault.jpg"></a>" title="The power of AI applied to agrochemical bioactivity" width="500" height="281" src="https://www.youtube-nocookie.com/embed/jv0Zt8X_yTU?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/the-power-of-ai-applied-to-agrochemical-bioactivity/">Watch the webinar</a></p> </div> </div> </div> </details> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">IFF</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Hear how Cerella predicted complex in vivo sensory responses, accurately, for International Flavors & Fragrances. </h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/t6FR-EgEs9I?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/t6FR-EgEs9I/maxresdefault.jpg"></a>" title="Imputation of sensory properties using deep learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/t6FR-EgEs9I?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in the <a href="https://doi.org/10.1007/s10822-021-00424-3" target="_blank" rel="noopener"><em>Journal of Computer-Aided Molecular Design</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/imputation-of-sensory-properties-using-deep-learning-webinar/">Watch the webinar</a></p> </div> </div> </div> </details> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">Genentech</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">A 76% reduction in the number of assays required. See how Cerella could transform Genentech’s kinase programmes.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/D7ReuGUpczI?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/D7ReuGUpczI/maxresdefault.jpg"></a>" title="Optimising kinase profiling programmes with deep learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/D7ReuGUpczI?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/optimising-kinase-profiling-programmes-with-deep-learning/">Watch the webinar</a></p> </div> </div> </div> </details> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">Open Source Malaria</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Learn how Cerella was able to find the only compound experimentally proven as active in the OSM competition.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/s0t6wsIy5KM?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/s0t6wsIy5KM/maxresdefault.jpg"></a>" title="AI-guided design of antimalarials with in vitro validation" width="500" height="281" src="https://www.youtube-nocookie.com/embed/s0t6wsIy5KM?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in the<a href="https://doi.org/10.1021/acs.jmedchem.1c00313" target="_blank" rel="noopener"> <em>Journal of Medicinal Chemistry</em></a>.</p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/ai-guided-design-of-antimalarials-with-in-vitro-validation/">Watch the webinar</a></p> </div> </div> </div> </details> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">Takeda Pharmaceuticals</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">Discover how Cerella handled 700,000 compounds and 1,000 experimental endpoints for Takeda Pharmaceuticals.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/Ks7supfZ6oQ?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/Ks7supfZ6oQ/maxresdefault.jpg"></a>" title="Large Scale Imputation of Drug Discovery Data using Deep Learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/Ks7supfZ6oQ?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p>Published in<a href="https://optibrium.com/knowledge-base/deep-imputation-on-large-scale-drug-discovery-data/"> <em>Applied AI Letters</em>.</a></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/large-scale-imputation-of-drug-discovery-data-using-deep-learning/">Watch the webinar</a></p> </div> </div> </div> </details> <details name="tabs-accordion-proven-success-in-drug-discovery" > <summary class="tabs__accordion-title">Undisclosed collaborator</summary> <div class="tabs__accordion-panel nflm"> <div class="tabs__tiles"> <div class="tabs__tile nflm"> <h3 class="is-style-typestyle-h6">See how Cerella helped obtain accurate <span>in vivo</span> PK profiles from limited <span>in vitro</span> ADME data for our collaborator.</h3> <p><iframe loading="lazy" srcdoc="<style>* { overflow: hidden; padding: 0; margin: 0; } html, body { height: 100%; } a { position: absolute; z-index: 9999; inset: 0; } img { position: absolute; inset: 0; width: 100%; height: 100%; object-fit: cover; object-position: center; } span { position: absolute; z-index: 1; inset: 50%; width: 84px; height: 84px; transform: translate(-50%, -50%); } span svg { display: block; width: 100%; }</style><a href="https://www.youtube-nocookie.com/embed/pUpZf-4w8gg?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1"><span><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 80 80" fill="none" role="img" aria-hidden="true" width="80" height="80"><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" fill="#F9F8F7" fill-opacity=".9"/><path d="M40 75.2c19.441 0 35.2-15.76 35.2-35.2C75.2 20.56 59.442 4.8 40 4.8 20.56 4.8 4.8 20.56 4.8 40c0 19.44 15.76 35.2 35.2 35.2Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/><path d="m30.4 56 27.2-16-27.2-16v32Z" stroke="#000" stroke-width="3.2" stroke-miterlimit="10"/></svg></span><img loading="lazy" src="https://img.youtube.com/vi/pUpZf-4w8gg/maxresdefault.jpg"></a>" title="Predicting pharmacokinetics from limited ADME data with deep learning" width="500" height="281" src="https://www.youtube-nocookie.com/embed/pUpZf-4w8gg?feature=oembed&autoplay=1&mute=1&modestbranding=1&playsinline=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p><a class="g-button is-yellow" href="https://optibrium.com/webinars/real-world-case-studies-predicting-pharmacokinetics-from-limited-adme-data-with-deep-learning/">Watch the webinar</a></p> </div> </div> </div> </details> </div> </div> </div> </section> <section id="resources" class="cards wp-block alignfull"> <div class="cards__outer"> <div class="cards__inner"> <div class="cards__header"> <h2 aria-label="Cerella resources" class="cards__heading is-style-typestyle-h1 has-tags">Cerella <span>resources</span></h2> </div> <div class="cards__items"> <article class="g-card is-knowledge-base is-drug-discovery-blog 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/blog-whats-the-difference-between-qsar-and-imputation-predictive-models/">What’s the difference between QSAR and imputation predictive models – which method should I use and when?</a> </h2> <div class="g-card__content"> <p>We’re often asked, “What’s the difference between QSAR and imputation models?”, so I’m going to explain how the methods differ, their advantages and disadvantages, and when each approach is applicable.</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"> 9 October 2024 </div> </div> </div> <div class="g-card__media img-fit"> <img decoding="async" width="768" height="479" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA5MDAgNTYxIiB3aWR0aD0iOTAwIiBoZWlnaHQ9IjU2MSIgZGF0YS11PSJodHRwcyUzQSUyRiUyRm9wdGlicml1bS5jb20lMkZ3cC1jb250ZW50JTJGdXBsb2FkcyUyRjIwMjQlMkYxMCUyRkNvbnRlbnQtYmxvZ3MtZmVhdHVyZS1pbWFnZS1NYXR0LVFTQVIucG5nIiBkYXRhLXc9IjkwMCIgZGF0YS1oPSI1NjEiPjwvc3ZnPg==" data-spai="1" class="attachment-medium_large size-medium_large" alt="" id="6069" 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/prediction-of-in-vivo-pharmacokinetic-parameters-and-time-exposure-curves-in-rats-using-machine-learning-from-the-chemical-structure/">Prediction of in vivo pharmacokinetic parameters and time – exposure curves in rats using machine learning from the chemical structure</a> </h2> <div class="g-card__content"> <p>This article is a collaboration with Intellegens, the University of Cambridge and AstraZeneca. It provides a proof-of-concept study in which Cerella™ is used to predict rat in vivo pharmacokinetic (PK) parameters and concentration–time PK profiles.</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/pharmacokinetics/" class="g-button is-small">Pharmacokinetics</a> </div> </div> <div class="g-card__meta"> 12 April 2022 </div> </div> </div> <div class="g-card__media img-fit"> <img decoding="async" width="768" height="278" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMDQ3IDM3OSIgd2lkdGg9IjEwNDciIGhlaWdodD0iMzc5IiBkYXRhLXU9Imh0dHBzJTNBJTJGJTJGb3B0aWJyaXVtLmNvbSUyRndwLWNvbnRlbnQlMkZ1cGxvYWRzJTJGMjAyNCUyRjA1JTJGTW9sLVBoYXJtLVByZWRpY3Rpb24tb2YtaW4tdml2by1QSy0yMDIyMzQtMS1lMTY5MDI2ODg1ODI5Ny5qcGciIGRhdGEtdz0iMTA0NyIgZGF0YS1oPSIzNzkiPjwvc3ZnPg==" data-spai="1" class="attachment-medium_large size-medium_large" alt="" id="1243" 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> </div> </div> </div> </section> <section id="contact-form" class="two-column-content wp-block alignwide"> <div class="two-column-content__outer"> <div class="two-column-content__column nflm is-first"> <h2 aria-label="Ready to transform to AI-guided drug discovery?" class="two-column-content__heading is-style-typestyle-h1 has-tags">Ready to transform <span>to AI-guided drug discovery?</span></h2> <div class="two-column-content__content nflm"> <p>Simply complete the form and our team will be happy to chat about how Cerella could improve your 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Cerella: Proven AI-guided drug discovery - Optibrium