The Commons 2.0 (TDC-2) 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. Our paper.
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The Commons 2.0 (TDC-2) 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. Our paper.
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-Jekyll2024-06-02T17:37:56-04:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikOn Knowing a Gene in Cell Systems2024-05-28T00:00:00-04:002024-05-28T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/05/28/KnowingAGene<p>We shed light on <a href="https://www.sciencedirect.com/science/article/pii/S2405471224001236">distributional gene representations and their potential applications in biology to characterize gene function from a broader and more holistic perspective.</a></p>Marinka ZitnikWe shed light on distributional gene representations and their potential applications in biology to characterize gene function from a broader and more holistic perspective.Biomedical AI Agents2024-04-04T00:00:00-04:002024-04-04T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/04/04/BIomedicalAIAgents<p>We envision <a href="https://arxiv.org/abs/2404.02831">‘AI scientists’ as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents</a> that integrate machine learning tools with experimental platforms.</p>Marinka ZitnikWe envision ‘AI scientists’ as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms.Efficient ML Seminar Series2024-03-23T00:00:00-04:002024-03-23T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/03/23/EfficientMLSeminar<p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p>Marinka ZitnikWe started a Harvard University Efficient ML Seminar Series. Congrats to Jonathan for spearheading this initiative. Harvard Magazine covered the first meeting focusing on LLMs.UniTS - Unified Time Series Model2024-03-04T00:00:00-05:002024-03-04T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/03/04/UniTS<p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p>Marinka ZitnikUniTS is a unified time series model that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. Project website.Weintraub Graduate Student Award2024-03-02T00:00:00-05:002024-03-02T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/03/02/WeintraubAward<p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p>Marinka ZitnikMichelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. News Story. Congratulations!PocketGen - Generating Full-Atom Ligand-Binding Protein Pockets2024-03-01T00:00:00-05:002024-03-01T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/03/01/PocketGen<p><a href="https://www.biorxiv.org/content/10.1101/2024.02.25.581968">PocketGen is a deep generative model</a> that generates residue sequence and full-atom structure of protein pockets, maximizing binding to ligands. <a href="https://zitniklab.hms.harvard.edu/projects/PocketGen/">Project website.</a></p>Marinka ZitnikPocketGen is a deep generative model that generates residue sequence and full-atom structure of protein pockets, maximizing binding to ligands. Project website.SPECTRA - Generalizability of Molecular AI2024-02-28T00:00:00-05:002024-02-28T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/02/28/SPECTRA-model-generalizability<p><a href="https://www.biorxiv.org/content/10.1101/2024.02.25.581982v1">SPECTRA is an approach for holistic evaluation of how AI models</a> generalize to new molecular datasets. <a href="https://zitniklab.hms.harvard.edu/projects/SPECTRA/">Project website.</a></p>Marinka ZitnikSPECTRA is an approach for holistic evaluation of how AI models generalize to new molecular datasets. Project website.Kaneb Fellowship Award2024-02-02T00:00:00-05:002024-02-02T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/02/02/KanebFellowship<p>The lab receives the <a href="#">John and Virginia Kaneb Fellowship Award at Harvard Medical School</a> to enhance research progress in the lab.</p>Marinka ZitnikThe lab receives the John and Virginia Kaneb Fellowship Award at Harvard Medical School to enhance research progress in the lab.NSF CAREER Award2024-02-02T00:00:00-05:002024-02-02T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/02/02/NSFCAREER<p>The lab receives the <a href="#">NSF CAREER Award</a> for our research in geometric deep learning to facilitate algorithmic and scientific advances in therapeutics.</p>Marinka ZitnikThe lab receives the NSF CAREER Award for our research in geometric deep learning to facilitate algorithmic and scientific advances in therapeutics.Dean’s Innovation Award in AI2024-02-01T00:00:00-05:002024-02-01T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/02/01/HMSDeansAI<p>The lab receives <a href="#">Dean’s Innovation Award for the Use of Artificial Intelligence in Research</a>. <a href="https://hms.harvard.edu/news/dean-announces-winners-inaugural-ai-grants">HMS News Story.</a></p>Marinka ZitnikThe lab receives Dean’s Innovation Award for the Use of Artificial Intelligence in Research. HMS News Story.
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+Jekyll2024-06-23T16:25:55-04:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikTDC-2: Multimodal Foundation for Therapeutics2024-06-23T00:00:00-04:002024-06-23T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/06/23/TDC2<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>Marinka ZitnikThe Commons 2.0 (TDC-2) 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. Our paper.On Knowing a Gene in Cell Systems2024-05-28T00:00:00-04:002024-05-28T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/05/28/KnowingAGene<p>We shed light on <a href="https://www.sciencedirect.com/science/article/pii/S2405471224001236">distributional gene representations and their potential applications in biology to characterize gene function from a broader and more holistic perspective.</a></p>Marinka ZitnikWe shed light on distributional gene representations and their potential applications in biology to characterize gene function from a broader and more holistic perspective.Biomedical AI Agents2024-04-04T00:00:00-04:002024-04-04T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/04/04/BIomedicalAIAgents<p>We envision <a href="https://arxiv.org/abs/2404.02831">‘AI scientists’ as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents</a> that integrate machine learning tools with experimental platforms.</p>Marinka ZitnikWe envision ‘AI scientists’ as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms.Efficient ML Seminar Series2024-03-23T00:00:00-04:002024-03-23T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/03/23/EfficientMLSeminar<p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p>Marinka ZitnikWe started a Harvard University Efficient ML Seminar Series. Congrats to Jonathan for spearheading this initiative. Harvard Magazine covered the first meeting focusing on LLMs.UniTS - Unified Time Series Model2024-03-04T00:00:00-05:002024-03-04T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/03/04/UniTS<p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p>Marinka ZitnikUniTS is a unified time series model that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. Project website.Weintraub Graduate Student Award2024-03-02T00:00:00-05:002024-03-02T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/03/02/WeintraubAward<p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p>Marinka ZitnikMichelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. News Story. Congratulations!PocketGen - Generating Full-Atom Ligand-Binding Protein Pockets2024-03-01T00:00:00-05:002024-03-01T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/03/01/PocketGen<p><a href="https://www.biorxiv.org/content/10.1101/2024.02.25.581968">PocketGen is a deep generative model</a> that generates residue sequence and full-atom structure of protein pockets, maximizing binding to ligands. <a href="https://zitniklab.hms.harvard.edu/projects/PocketGen/">Project website.</a></p>Marinka ZitnikPocketGen is a deep generative model that generates residue sequence and full-atom structure of protein pockets, maximizing binding to ligands. Project website.SPECTRA - Generalizability of Molecular AI2024-02-28T00:00:00-05:002024-02-28T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/02/28/SPECTRA-model-generalizability<p><a href="https://www.biorxiv.org/content/10.1101/2024.02.25.581982v1">SPECTRA is an approach for holistic evaluation of how AI models</a> generalize to new molecular datasets. <a href="https://zitniklab.hms.harvard.edu/projects/SPECTRA/">Project website.</a></p>Marinka ZitnikSPECTRA is an approach for holistic evaluation of how AI models generalize to new molecular datasets. Project website.Kaneb Fellowship Award2024-02-02T00:00:00-05:002024-02-02T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/02/02/KanebFellowship<p>The lab receives the <a href="#">John and Virginia Kaneb Fellowship Award at Harvard Medical School</a> to enhance research progress in the lab.</p>Marinka ZitnikThe lab receives the John and Virginia Kaneb Fellowship Award at Harvard Medical School to enhance research progress in the lab.NSF CAREER Award2024-02-02T00:00:00-05:002024-02-02T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/02/02/NSFCAREER<p>The lab receives the <a href="#">NSF CAREER Award</a> for our research in geometric deep learning to facilitate algorithmic and scientific advances in therapeutics.</p>Marinka ZitnikThe lab receives the NSF CAREER Award for our research in geometric deep learning to facilitate algorithmic and scientific advances in therapeutics.
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AI for Science | Therapeutic Science
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Jun 2024: TDC-2: Multimodal Foundation for Therapeutics
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The Commons 2.0 (TDC-2) 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. Our paper.
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AI for Science | Therapeutic Science
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Jun 2023: Our Group is Joining the Kempner Institute
Jun 2024: TDC-2: Multimodal Foundation for Therapeutics
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The Commons 2.0 (TDC-2) 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. Our paper.
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The Commons 2.0 (TDC-2) 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. Our paper.
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PhD Forum (ECML/PKDD 2020)
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Jun 2023: Our Group is Joining the Kempner Institute
Jun 2024: TDC-2: Multimodal Foundation for Therapeutics
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The Commons 2.0 (TDC-2) 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. Our paper.
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Jun 2022: Broadly Generalizable Pre-Training Approach
Xiang Zhang (Postdoctoral Fellow, HMS → Assistant Professor, UNC Charlotte)
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Chirag Agarwal (Postdoctoral Fellow, HMS → Research Scientist, Adobe Research)
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Xiang Zhang (Postdoctoral Fellow → Assistant Professor, UNC Charlotte)
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Chirag Agarwal (Postdoctoral Fellow, HMS → Assistant Professor, University of Virginia)
Josh Pan (Postdoctoral Fellow, Broad Institute → Research Scientist, DeepMind)
Michelle Lu (Harvard College)
Raunak Chowdhuri (MIT)
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Lab alumni
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Jun 2024: TDC-2: Multimodal Foundation for Therapeutics
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The Commons 2.0 (TDC-2) 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. Our paper.
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Jun 2023: Our Group is Joining the Kempner Institute
Jun 2024: TDC-2: Multimodal Foundation for Therapeutics
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The Commons 2.0 (TDC-2) 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. Our paper.
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Jun 2023: Our Group is Joining the Kempner Institute
Jun 2024: TDC-2: Multimodal Foundation for Therapeutics
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The Commons 2.0 (TDC-2) 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. Our paper.
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The Commons 2.0 (TDC-2) 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. Our paper.
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Jun 2023: Our Group is Joining the Kempner Institute
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The Commons 2.0 (TDC-2) 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. Our paper.
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The Commons 2.0 (TDC-2) 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. Our paper.
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