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!