Recent News

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Jul 2024:   Digital Twins as Global Health and Disease Models of Individuals

Paper on digitial twins outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals.

Jul 2024:   Three Papers: TrialBench, 3D Structure Design, LLM Editing

Jun 2024:   TDC-2: Multimodal Foundation for Therapeutics

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.

May 2024:   Broad MIA: Protein Language Models

Apr 2024:   Biomedical AI Agents

Mar 2024:   Efficient ML Seminar Series

We started a Harvard University Efficient ML Seminar Series. Congrats to Jonathan for spearheading this initiative. Harvard Magazine covered the first meeting focusing on LLMs.

Mar 2024:   UniTS - Unified Time Series Model

UniTS 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.

Mar 2024:   Weintraub Graduate Student Award

Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. News Story. Congratulations!

Mar 2024:   PocketGen - Generating Full-Atom Ligand-Binding Protein Pockets

PocketGen is a deep generative model that generates residue sequence and full-atom structure of protein pockets, maximizing binding to ligands. Project website.

Feb 2024:   SPECTRA - Generalizability of Molecular AI

Feb 2024:   Kaneb Fellowship Award

The lab receives the John and Virginia Kaneb Fellowship Award at Harvard Medical School to enhance research progress in the lab.

Feb 2024:   NSF CAREER Award

The lab receives the NSF CAREER Award for our research in geometric deep learning to facilitate algorithmic and scientific advances in therapeutics.

Feb 2024:   Dean’s Innovation Award in AI

Jan 2024:   AI's Prospects in Nature Machine Intelligence

We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.

Jan 2024:   Combinatorial Therapeutic Perturbations

New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.

Nov 2023:   Next Generation of Therapeutics Commons

Oct 2023:   Structure-Based Drug Design

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

Oct 2023:   Graph AI in Medicine

Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

Sep 2023:   New papers accepted at NeurIPS

Sep 2023:   Future Directions in Network Biology

Excited to share our perspectives on current and future directions in network biology.

Aug 2023:   Scientific Discovery in the Age of AI

Jul 2023:   PINNACLE - Contextual AI protein model

PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

Jun 2023:   Our Group is Joining the Kempner Institute

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

Jun 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.

Jun 2023:   On Pretraining in Nature Machine Intelligence

May 2023:   Congratulations to Ada and Michelle

Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

Apr 2023:   Universal Domain Adaptation at ICML 2023

New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

Apr 2023:   Celebrating Achievements of Our Undergrads

Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

Apr 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

Apr 2023:   New Study in Nature Machine Intelligence

New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

Mar 2023:   Precision Health in Nature Machine Intelligence

New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

Mar 2023:   Self-Driving Labs in Nature Machine Intelligence

Mar 2023:   TxGNN - Zero-shot prediction of therapeutic use

Mar 2023:   GraphXAI published in Scientific Data

Feb 2023:   Welcoming New Postdoctoral Fellows

A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

Feb 2023:   New Preprint on Distribution Shifts

Feb 2023:   PrimeKG published in Scientific Data

Jan 2023:   GNNDelete published at ICLR 2023

Jan 2023:   New Network Principle for Molecular Phenotypes

Dec 2022:   Can we shorten rare disease diagnostic odyssey?

New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

Nov 2022:   Can AI transform the way we discover new drugs?

Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

Oct 2022:   New Paper in Nature Biomedical Engineering

New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

Sep 2022:   New Paper in Nature Chemical Biology

Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.

Sep 2022:   Self-Supervised Pre-Training at NeurIPS 2022

New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.

Sep 2022:   Best Paper Honorable Mention Award at IEEE VIS

Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.

Sep 2022:   Multimodal Representation Learning with Graphs

Aug 2022:   On Graph AI for Precision Medicine

The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.

Aug 2022:   Evaluating Explainability for GNNs

New preprint! We introduce a resource for broad evaluation of the quality and reliability of GNN explanations, addressing challenges and providing solutions for GNN explainability. Project website.

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