New paper: Multimodal AI approach for designing combination therapies with improved predictive accuracy and clinical relevance. [Project website]

KGARevion is an AI agent designed for complex biomedical QA that integrates the non-codified knowledge of LLMs with the structured, codified knowledge found in knowledge graphs. [ICLR 2025 publication]

Meet MedTok, a multimodal medical code tokenizer that transforms how AI understands structured medical data. By integrating textual descriptions and relational contexts, MedTok enhances tokenization for transformer-based models—powering everything from EHR foundation models to medical QA. [Project website]

What if we could anticipate molecular and medical changes before they happen? Introducing CLEF, an approach for counterfactual generation in biological and medical sequence models. [Project website]

New paper on the role of digital twins as global health and disease learning models for preventive and personalized medicine.

New papers on test-time interventions in language models and knowledge graph based LLM agents accepted to ICLR. [KGARevion]

Excited to share our new graduate course on Artificial Intelligence in Medicine 2.

Thanks to Kempner Institute for highlighting our latest research, ProCyon, our protein-text foundation model for modeling protein functions.

New Voices piece in Cell Systems: How will computational protein design change biotechnology and therapeutic development?

New paper: ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes. [Project website] [Code]

New paper: A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies. (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.

Are biomedical AI models truly as smart as they seem? SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity. SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.

Congratulations to Ayush Noori on being named a Rhodes Scholar! Such an incredible achievement!

PocketGen is a multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.

We envision “AI scientists” as AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.

New paper on activity-cliff informed contrastive learning for molecular property prediction.

New paper introducing a knowledge graph agent for complex, knowledge-intensive medical reasoning.

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.

Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes] [NVIDIA] [Kempner Institute] [Harvard Crimson]

Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.

Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.

PINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website.

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

Graph diffusion convolution is a geometric deep learning architecture that aggregates information from higher-order network neighbors through a generalized graph diffusion to enhance model robustness to noisy and incomplete datasets. Paper at ICML.

New papers on TrialBench with AI-ready clinical trial datasets, structure-based drug design benchmark, and composable interventions for language models.

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.

Check out our Broad’s seminars on Multimodal protein language models for deciphering protein function.

We shed light on distributional gene representations and their potential applications in biology to characterize gene function from a broader and more holistic perspective.

We 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.

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.

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.

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

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

SPECTRA is an approach for holistic evaluation of how AI models generalize to new molecular datasets. Project website.

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

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

The lab receives Dean’s Innovation Award for the Use of Artificial Intelligence in Research. HMS News Story.

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.

Prof. Zitnik discussed the transformative role of ChatGPT and emerging AI models in advancing science and biomedical research with Nature.

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

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

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.

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.

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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

New paper on the role of artificial intelligence in scientific discovery is published in Nature.

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

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

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

Excited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.