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TDC-2: Multimodal Foundation for Therapeutics

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Published: Jun 23, 2024

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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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Zitnik Lab +  ·  Artificial Intelligence in Medicine and Science +  ·  Harvard +  ·  Department of Biomedical Informatics

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Jun 2024:   TDC-2: Multimodal Foundation for Therapeutics

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Jun 2023:   Our Group is Joining the Kempner Institute

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Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

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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

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diff --git a/feed.xml b/feed.xml index f156ef97..98cb617a 100644 --- a/feed.xml +++ b/feed.xml @@ -1 +1 @@ -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. \ No newline at end of file +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. \ No newline at end of file diff --git a/img/intae_moon.png b/img/intae_moon.png new file mode 100644 index 00000000..0d8786cd Binary files /dev/null and b/img/intae_moon.png differ diff --git a/img/pengwei_sui.png b/img/pengwei_sui.png new file mode 100644 index 00000000..c142172d Binary files /dev/null and b/img/pengwei_sui.png differ diff --git a/img/zhenglun_kong.png b/img/zhenglun_kong.png new file mode 100644 index 00000000..ca7317fd Binary files /dev/null and b/img/zhenglun_kong.png differ diff --git a/index.html b/index.html index 23296590..fd3b6668 100644 --- a/index.html +++ b/index.html @@ -177,6 +177,34 @@

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

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Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

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Visitors, interns, and short-t
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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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Visitors, interns, and short-t

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Jun 2023:   Our Group is Joining the Kempner Institute

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Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

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PhD Forum (ECML/PKDD 2020)

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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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PhD Forum (ECML/PKDD 2020)

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Jun 2023:   Our Group is Joining the Kempner Institute

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Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

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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 2022:   Broadly Generalizable Pre-Training Approach

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Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

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Jun 2022:   Broadly Generalizable Pre-Training Approach

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Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

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Jan 2021:   Tutorial on ML for Drug Development

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We will present a tutorial on ML/AI for drug discovery and development at IJCAI conference. See the tutorial website.

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Jan 2021:   Tutorial on ML for Drug Development

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We will present a tutorial on ML/AI for drug discovery and development at IJCAI conference. See the tutorial website.

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Xiaorui Su

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Jonathan Richard Schwarz

Postdoctoral Fellow

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Xiang Lin

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Zhenglun Kong

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Wanxiang Shen

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Ruth Johnson

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Postdoctoral Fellow
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Ruth Johnson

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Postdoctoral Fellow
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Wanxiang Shen

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Postdoctoral Fellow

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Tianlong Chen

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Xiang Lin

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Jonathan Richard Schwarz

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Xiaorui Su

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Zaixi Zhang

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Intae Moon

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Postdoctoral Fellow

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Lei Huang

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Owen Queen

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Research Associate

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Owen Queen

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Alejandro Velez Arce

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Alejandro Velez Arce

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Pengwei Sui

Research Associate

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Michelle Dai

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Graduate Researcher

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Zaixi Zhang

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Visiting PhD Student

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Kushan Weerakoon

Undergraduate Researcher
Harvard

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Ayush Noori

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Ivy Liang

Undergraduate Researcher
Harvard

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Kushan Weerakoon

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Ayush Noori

Undergraduate Researcher
Harvard

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Associate members

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Lab alumni

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  • Varun Ullanat (HMS)
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  • Tianlong Chen (Postdoctoral Fellow → Assistant Professor, University of North Carolina at Chapel Hill)
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Lab alumni

  • Marie Zhang (HMS)
  • Man Qing Liang (HMS → Data Scientist, Dandelion)
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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)
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    Lab alumni

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    Jun 2024:   TDC-2: Multimodal Foundation for Therapeutics

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