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<!DOCTYPE html>
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<h4 class="has-text-white">Machine Learning Foundations</h4>
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Our overarching goal is to lay the foundations for AI that contribute to the scientific understanding of medicine and therapeutic design, eventually enabling AI to learn on its own and acquire knowledge autonomously.
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We focus on foundational innovation in artificial intelligence and machine learning with an emphasis on AI systems that are informed by geometry, structure, and grounded in medical knowledge. This involves building AI models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization.
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The state of a person is described with increasing precision incorporating modalities like genetic code, cellular atlases, molecular datasets, and therapeutics—the challenge is how to reason over these data to develop powerful disease diagnostics and empower new kinds of therapies. Our research creates new avenues for fusing knowledge and patient data to give the right patient the right treatment at the right time and have medicinal effects that are consistent from person to person and with results in the laboratory.
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<h4 class="has-text-white">AI for Science | Scientific Discovery and Therapeutic Science</h4>
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For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. We are using AI to change that. The natural world is interconnected, from the various facets of genome regulation to the molecular and organismal levels. These interactions across different levels yield a bewildering degree of complexity. Our research seeks to disentangle this complexity, developing AI models that advance drug design and help develop new kinds of therapies.
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<!-- <a class="card-header-title" href="/2025/01/05/ProCyonKempnerBlog/">ProCyon AI Highlighted by Kempner</a>-->
<p class="card-header-title">Jan 2025: <span class="has-text-primary">ProCyon AI Highlighted by Kempner</span></p>
<!-- <p class="card-header-item">Jan 2025</p>-->
<!-- <p class="card-footer-item">Jan 5, 2025</p>-->
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<!-- <p><p>Thanks to Kempner Institute for highlighting our latest research, <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/procyon-a-multimodal-foundation-model-for-protein-phenotypes/">ProCyon, a multimodal foundation model for protein phenotypes.</a></p>
</p>-->
<p>Thanks to Kempner Institute for highlighting our latest research, <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/procyon-a-multimodal-foundation-model-for-protein-phenotypes/">ProCyon, a multimodal foundation model for protein phenotypes.</a></p>
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<!-- <p class="card-footer-item">Published: Jan 5, 2025</p>-->
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<!-- <a class="card-header-title" href="/2025/01/05/CellSystemsVoices/">AI Design of Proteins for Therapeutics</a>-->
<p class="card-header-title">Jan 2025: <span class="has-text-primary">AI Design of Proteins for Therapeutics</span></p>
<!-- <p class="card-header-item">Jan 2025</p>-->
<!-- <p class="card-footer-item">Jan 5, 2025</p>-->
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<!-- <p><p>New Voices piece in Cell Systems: <a href="https://www.cell.com/cell-systems/abstract/S2405-4712(24)00309-0">How will computational protein design change biotechnology and therapeutic development?</a></p>
</p>-->
<p>New Voices piece in Cell Systems: <a href="https://www.cell.com/cell-systems/abstract/S2405-4712(24)00309-0">How will computational protein design change biotechnology and therapeutic development?</a></p>
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<!-- <p class="card-footer-item">Published: Jan 5, 2025</p>-->
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<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>-->
<p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p>
<!-- <p class="card-header-item">Dec 2024</p>-->
<!-- <p class="card-footer-item">Dec 16, 2024</p>-->
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<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p>
</p>-->
<p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p>
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<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>-->
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<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>-->
<p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p>
<!-- <p class="card-header-item">Dec 2024</p>-->
<!-- <p class="card-footer-item">Dec 7, 2024</p>-->
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<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p>
</p>-->
<p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p>
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<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>-->
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<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>-->
<p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p>
<!-- <p class="card-header-item">Dec 2024</p>-->
<!-- <p class="card-footer-item">Dec 7, 2024</p>-->
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<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p>
</p>-->
<p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p>
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<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>-->
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<!-- <a class="card-header-title" href="/2024/11/17/RhodesScholar/">Ayush Noori Selected as a Rhodes Scholar</a>-->
<p class="card-header-title">Nov 2024: <span class="has-text-primary">Ayush Noori Selected as a Rhodes Scholar</span></p>
<!-- <p class="card-header-item">Nov 2024</p>-->
<!-- <p class="card-footer-item">Nov 17, 2024</p>-->
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<!-- <p><p>Congratulations to <a href="https://www.thecrimson.com/article/2024/11/18/rhodes-scholars-announced-harvard-students/">Ayush Noori on being named a Rhodes Scholar</a>! Such an incredible achievement!</p>
</p>-->
<p>Congratulations to <a href="https://www.thecrimson.com/article/2024/11/18/rhodes-scholars-announced-harvard-students/">Ayush Noori on being named a Rhodes Scholar</a>! Such an incredible achievement!</p>
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<!-- <p class="card-footer-item">Published: Nov 17, 2024</p>-->
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<!-- <a class="card-header-title" href="/2024/11/15/PocketGen/">PocketGen in Nature Machine Intelligence</a>-->
<p class="card-header-title">Nov 2024: <span class="has-text-primary">PocketGen in Nature Machine Intelligence</span></p>
<!-- <p class="card-header-item">Nov 2024</p>-->
<!-- <p class="card-footer-item">Nov 15, 2024</p>-->
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<!-- <p><p>PocketGen is a <a href="https://www.nature.com/articles/s42256-024-00920-9">multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</a></p>
</p>-->
<p>PocketGen is a <a href="https://www.nature.com/articles/s42256-024-00920-9">multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</a></p>
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<!-- <p class="card-footer-item">Published: Nov 15, 2024</p>-->
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<!-- <a class="card-header-title" href="/2024/11/01/AIScientist/">Biomedical AI Agents in Cell</a>-->
<p class="card-header-title">Nov 2024: <span class="has-text-primary">Biomedical AI Agents in Cell</span></p>
<!-- <p class="card-header-item">Nov 2024</p>-->
<!-- <p class="card-footer-item">Nov 1, 2024</p>-->
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<!-- <p><p>We envision “AI scientists” as <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5">AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</a></p>
</p>-->
<p>We envision “AI scientists” as <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5">AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</a></p>
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<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>-->
<p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p>
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<p>New paper on <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6470c963be16ad5c57f5526c">activity-cliff informed contrastive learning for molecular property prediction.</a></p>
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<!-- <a class="card-header-title" href="/2024/10/09/KGARevion/">Knowledge Graph Agent for Medical Reasoning</a>-->
<p class="card-header-title">Oct 2024: <span class="has-text-primary">Knowledge Graph Agent for Medical Reasoning</span></p>
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<!-- <p class="card-footer-item">Oct 9, 2024</p>-->
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<!-- <p><p>New paper introducing a <a href="https://arxiv.org/abs/2410.04660">knowledge graph agent for complex, knowledge-intensive medical reasoning.</a></p>
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<p>New paper introducing a <a href="https://arxiv.org/abs/2410.04660">knowledge graph agent for complex, knowledge-intensive medical reasoning.</a></p>
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<!-- <a class="card-header-title" href="/2024/09/27/NeurIPS2024Papers/">Three Papers Accepted to NeurIPS</a>-->
<p class="card-header-title">Sep 2024: <span class="has-text-primary">Three Papers Accepted to NeurIPS</span></p>
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<!-- <p><p>Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</p>
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<p>Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</p>
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<!-- <a class="card-header-title" href="/2024/09/25/TxGNNNatureMedicine/">TxGNN Published in Nature Medicine</a>-->
<p class="card-header-title">Sep 2024: <span class="has-text-primary">TxGNN Published in Nature Medicine</span></p>
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<!-- <p><p>Graph foundation model for drug repurposing published in <a href="https://www.nature.com/articles/s41591-024-03233-x">Nature Medicine</a>. <a href="https://news.harvard.edu/gazette/story/2024/09/using-ai-to-repurpose-existing-drugs-for-treatment-of-rare-diseases/">[Harvard Gazette]</a> <a href="https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases">[Harvard Medicine News]</a> <a href="https://www.forbes.com/sites/greglicholai/2024/09/26/ai-tool-speeds-drug-repurposing-and-its-free/">[Forbes]</a> <a href="https://developer.nvidia.com/blog/ai-uses-zero-shot-learning-to-find-existing-drugs-for-treating-rare-diseases/">[NVIDIA]</a> <a href="https://kempnerinstitute.harvard.edu/news/txgnn-ai-dr-house-for-disease-treatment/">[Kempner Institute]</a> <a href="https://www.thecrimson.com/article/2024/10/9/drug-repurposing-ai-model/">[Harvard Crimson]</a></p>
</p>-->
<p>Graph foundation model for drug repurposing published in <a href="https://www.nature.com/articles/s41591-024-03233-x">Nature Medicine</a>. <a href="https://news.harvard.edu/gazette/story/2024/09/using-ai-to-repurpose-existing-drugs-for-treatment-of-rare-diseases/">[Harvard Gazette]</a> <a href="https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases">[Harvard Medicine News]</a> <a href="https://www.forbes.com/sites/greglicholai/2024/09/26/ai-tool-speeds-drug-repurposing-and-its-free/">[Forbes]</a> <a href="https://developer.nvidia.com/blog/ai-uses-zero-shot-learning-to-find-existing-drugs-for-treating-rare-diseases/">[NVIDIA]</a> <a href="https://kempnerinstitute.harvard.edu/news/txgnn-ai-dr-house-for-disease-treatment/">[Kempner Institute]</a> <a href="https://www.thecrimson.com/article/2024/10/9/drug-repurposing-ai-model/">[Harvard Crimson]</a></p>
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<!-- <a class="card-header-title" href="/2024/08/28/GraphAI/">Graph AI in Medicine</a>-->
<p class="card-header-title">Aug 2024: <span class="has-text-primary">Graph AI in Medicine</span></p>
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<!-- <p class="card-footer-item">Aug 28, 2024</p>-->
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<!-- <p><p>Excited to share a new perspective on <a href="https://go.shr.lc/4g0KpLV">Graph Artificial Intelligence in Medicine</a> in Annual Reviews.</p>
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<p>Excited to share a new perspective on <a href="https://go.shr.lc/4g0KpLV">Graph Artificial Intelligence in Medicine</a> in Annual Reviews.</p>
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<!-- <a class="card-header-title" href="/2024/08/15/PINNACLENews/">How Proteins Behave in Context</a>-->
<p class="card-header-title">Aug 2024: <span class="has-text-primary">How Proteins Behave in Context</span></p>
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<!-- <p class="card-footer-item">Aug 15, 2024</p>-->
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<!-- <p><p><a href="https://hms.harvard.edu/news/new-ai-tool-captures-how-proteins-behave-context">Harvard Medicine News</a> on our new AI tool that captures how proteins behave in context. <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/context-matters-for-foundation-models-in-biology/">Kempner Institute</a> on how context matters for foundation models in biology.</p>
</p>-->
<p><a href="https://hms.harvard.edu/news/new-ai-tool-captures-how-proteins-behave-context">Harvard Medicine News</a> on our new AI tool that captures how proteins behave in context. <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/context-matters-for-foundation-models-in-biology/">Kempner Institute</a> on how context matters for foundation models in biology.</p>
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<!-- <a class="card-header-title" href="/2024/07/27/PINNACLENatureMethods/">PINNACLE in Nature Methods</a>-->
<p class="card-header-title">Jul 2024: <span class="has-text-primary">PINNACLE in Nature Methods</span></p>
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<!-- <p><p>PINNACLE contextual AI model is published in Nature Methods. <a href="https://www.nature.com/articles/s41592-024-02341-3">Paper.</a> <a href="https://www.nature.com/articles/s41592-024-02342-2">Research Briefing.</a> <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>
</p>-->
<p>PINNACLE contextual AI model is published in Nature Methods. <a href="https://www.nature.com/articles/s41592-024-02341-3">Paper.</a> <a href="https://www.nature.com/articles/s41592-024-02342-2">Research Briefing.</a> <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>
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<!-- <a class="card-header-title" href="/2024/07/15/DigitalTwins/">Digital Twins as Global Health and Disease Models of Individuals</a>-->
<p class="card-header-title">Jul 2024: <span class="has-text-primary">Digital Twins as Global Health and Disease Models of Individuals</span></p>
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<!-- <p class="card-footer-item">Jul 15, 2024</p>-->
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<!-- <p><p><a href="https://www.preprints.org/manuscript/202406.0357/">Paper on digitial twins</a> outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals.</p>
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<p><a href="https://www.preprints.org/manuscript/202406.0357/">Paper on digitial twins</a> outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals.</p>
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<!-- <a class="card-header-title" href="/2024/07/14/GraphAdversarialDiffusion/">Graph Diffusion Convolutions at ICML</a>-->
<p class="card-header-title">Jul 2024: <span class="has-text-primary">Graph Diffusion Convolutions at ICML</span></p>
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<!-- <p><p><a href="https://arxiv.org/abs/2406.02059">Graph diffusion convolution is a geometric deep learning architecture that aggregates information from higher-order network neighbors through a generalized graph diffusion</a> to enhance model robustness to noisy and incomplete datasets. <a href="https://arxiv.org/abs/2406.02059">Paper at ICML.</a></p>
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<p><a href="https://arxiv.org/abs/2406.02059">Graph diffusion convolution is a geometric deep learning architecture that aggregates information from higher-order network neighbors through a generalized graph diffusion</a> to enhance model robustness to noisy and incomplete datasets. <a href="https://arxiv.org/abs/2406.02059">Paper at ICML.</a></p>
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<!-- <a class="card-header-title" href="/2024/07/13/TrialBenchLLM3DStructure/">Three Papers: TrialBench, 3D Structure Design, LLM Editing</a>-->
<p class="card-header-title">Jul 2024: <span class="has-text-primary">Three Papers: TrialBench, 3D Structure Design, LLM Editing</span></p>
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<!-- <p><p>New papers on <a href="https://arxiv.org/abs/2407.00631">TrialBench with AI-ready clinical trial datasets</a>, <a href="https://arxiv.org/abs/2406.03403">structure-based drug design benchmark</a>, and <a href="https://arxiv.org/abs/2407.06483">composable interventions for language models</a>.</p>
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<p>New papers on <a href="https://arxiv.org/abs/2407.00631">TrialBench with AI-ready clinical trial datasets</a>, <a href="https://arxiv.org/abs/2406.03403">structure-based drug design benchmark</a>, and <a href="https://arxiv.org/abs/2407.06483">composable interventions for language models</a>.</p>
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<!-- <a class="card-header-title" href="/2024/06/23/TDC2/">TDC-2: Multimodal Foundation for Therapeutics</a>-->
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<!-- <p><p><a href="https://tdcommons.ai/">The Commons 2.0 (TDC-2)</a> is an overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new tasks and benchmarks. <a href="https://www.biorxiv.org/content/10.1101/2024.06.12.598655v2">Our paper.</a></p>
</p>-->
<p><a href="https://tdcommons.ai/">The Commons 2.0 (TDC-2)</a> is an overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new tasks and benchmarks. <a href="https://www.biorxiv.org/content/10.1101/2024.06.12.598655v2">Our paper.</a></p>
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<!-- <a class="card-header-title" href="/2024/05/29/BroadMIA/">Broad MIA: Protein Language Models</a>-->
<p class="card-header-title">May 2024: <span class="has-text-primary">Broad MIA: Protein Language Models</span></p>
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<!-- <p class="card-footer-item">May 29, 2024</p>-->
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<!-- <p><p>Check out our Broad’s seminars on <a href="https://youtu.be/LcLmvtXHI1s?si=GABqGdFt5ze9leT_">Multimodal protein language models for deciphering protein function.</a></p>
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<p>Check out our Broad’s seminars on <a href="https://youtu.be/LcLmvtXHI1s?si=GABqGdFt5ze9leT_">Multimodal protein language models for deciphering protein function.</a></p>
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