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href="/search/advanced?terms-0-term=Kenlay%2C+H&amp;terms-0-field=author&amp;size=50&amp;order=-announced_date_first">Advanced Search</a> </div> </div> <input type="hidden" name="order" value="-announced_date_first"> <input type="hidden" name="size" value="50"> </form> <div class="level breathe-horizontal"> <div class="level-left"> <form method="GET" action="/search/"> <div style="display: none;"> <select id="searchtype" name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option 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tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> ABodyBuilder3: Improved and scalable antibody structure predictions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&amp;query=Kenlay%2C+H">Henry Kenlay</a>, <a href="/search/q-bio?searchtype=author&amp;query=Dreyer%2C+F+A">Fr茅d茅ric A. Dreyer</a>, <a href="/search/q-bio?searchtype=author&amp;query=Cutting%2C+D">Daniel Cutting</a>, <a href="/search/q-bio?searchtype=author&amp;query=Nissley%2C+D">Daniel Nissley</a>, <a href="/search/q-bio?searchtype=author&amp;query=Deane%2C+C+M">Charlotte M. Deane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.20863v1-abstract-short" style="display: inline;"> Accurate prediction of antibody structure is a central task in the design and development of monoclonal antibodies, notably to understand both their developability and their binding properties. In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20863v1-abstract-full').style.display = 'inline'; document.getElementById('2405.20863v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20863v1-abstract-full" style="display: none;"> Accurate prediction of antibody structure is a central task in the design and development of monoclonal antibodies, notably to understand both their developability and their binding properties. In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20863v1-abstract-full').style.display = 'none'; document.getElementById('2405.20863v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 3 figures, 3 tables, code available at https://github.com/Exscientia/ABodyBuilder3, weights and data available at https://zenodo.org/records/11354577</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.17889">arXiv:2403.17889</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2403.17889">pdf</a>, <a href="https://arxiv.org/format/2403.17889">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Large scale paired antibody language models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&amp;query=Kenlay%2C+H">Henry Kenlay</a>, <a href="/search/q-bio?searchtype=author&amp;query=Dreyer%2C+F+A">Fr茅d茅ric A. Dreyer</a>, <a href="/search/q-bio?searchtype=author&amp;query=Kovaltsuk%2C+A">Aleksandr Kovaltsuk</a>, <a href="/search/q-bio?searchtype=author&amp;query=Miketa%2C+D">Dom Miketa</a>, <a href="/search/q-bio?searchtype=author&amp;query=Pires%2C+D">Douglas Pires</a>, <a href="/search/q-bio?searchtype=author&amp;query=Deane%2C+C+M">Charlotte M. Deane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.17889v1-abstract-short" style="display: inline;"> Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the design of better therapeutics has been con&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17889v1-abstract-full').style.display = 'inline'; document.getElementById('2403.17889v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.17889v1-abstract-full" style="display: none;"> Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the design of better therapeutics has been constrained by the sheer volume and complexity of the data. To address this challenge, we present IgBert and IgT5, the best performing antibody-specific language models developed to date which can consistently handle both paired and unpaired variable region sequences as input. These models are trained comprehensively using the more than two billion unpaired sequences and two million paired sequences of light and heavy chains present in the Observed Antibody Space dataset. We show that our models outperform existing antibody and protein language models on a diverse range of design and regression tasks relevant to antibody engineering. This advancement marks a significant leap forward in leveraging machine learning, large scale data sets and high-performance computing for enhancing antibody design for therapeutic development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17889v1-abstract-full').style.display = 'none'; document.getElementById('2403.17889v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 2 figures, 6 tables, model weights available at https://zenodo.org/doi/10.5281/zenodo.10876908</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.19513">arXiv:2310.19513</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2310.19513">pdf</a>, <a href="https://arxiv.org/format/2310.19513">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Inverse folding for antibody sequence design using deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&amp;query=Dreyer%2C+F+A">Fr茅d茅ric A. Dreyer</a>, <a href="/search/q-bio?searchtype=author&amp;query=Cutting%2C+D">Daniel Cutting</a>, <a href="/search/q-bio?searchtype=author&amp;query=Schneider%2C+C">Constantin Schneider</a>, <a href="/search/q-bio?searchtype=author&amp;query=Kenlay%2C+H">Henry Kenlay</a>, <a href="/search/q-bio?searchtype=author&amp;query=Deane%2C+C+M">Charlotte M. Deane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.19513v1-abstract-short" style="display: inline;"> We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for antibody structures and outperforms generic protein models on sequence recovery and structure robustness when applied on antibodies, with notable improvement on the hypervariable CDR-H3 loop. We study the ca&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19513v1-abstract-full').style.display = 'inline'; document.getElementById('2310.19513v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.19513v1-abstract-full" style="display: none;"> We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for antibody structures and outperforms generic protein models on sequence recovery and structure robustness when applied on antibodies, with notable improvement on the hypervariable CDR-H3 loop. We study the canonical conformations of complementarity-determining regions and find improved encoding of these loops into known clusters. Finally, we consider the applications of our model to drug discovery and binder design and evaluate the quality of proposed sequences using physics-based methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19513v1-abstract-full').style.display = 'none'; document.getElementById('2310.19513v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2023 ICML Workshop on Computational Biology, model weights available at https://zenodo.org/record/8164693</span> </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <span class="help" 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