Nov 2024: Biomedical AI Agents in Cell
Oct 2024: Activity Cliffs in Molecular Property Prediction
Oct 2024: Knowledge Graph Agent for Medical Reasoning
New paper introducing a knowledge graph agent for complex, knowledge-intensive medical reasoning.
Sep 2024: Three Papers Accepted to NeurIPS
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.
Sep 2024: TxGNN Published in Nature Medicine
Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes] [NVIDIA]
Aug 2024: Graph AI in Medicine
Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
Aug 2024: How Proteins Behave in Context
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.
Jul 2024: PINNACLE in Nature Methods
PINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website.
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: Graph Diffusion Convolutions at ICML
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.
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
Check out our Broad’s seminars on Multimodal protein language models for deciphering protein function.
May 2024: On Knowing a Gene in Cell Systems
Apr 2024: Biomedical AI Agents
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.
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
SPECTRA is an approach for holistic evaluation of how AI models generalize to new molecular datasets. Project website.
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
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.
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
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.
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
New paper on the role of artificial intelligence in scientific discovery is published in Nature.
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
Excited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.
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
New paper with NASA in Nature Machine Intelligence on biological research and self-driving labs in deep space supported by artificial intelligence.
Mar 2023: TxGNN - Zero-shot prediction of therapeutic use
New study on zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design. Check out our project website and TxGNN Explorer.
Mar 2023: GraphXAI published in Scientific Data
Our approach evaluating explainability of geometric deep learning models is published in Scientific Data. Project website.
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
Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.
Jan 2023: GNNDelete published at ICLR 2023
New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.
Jan 2023: New Network Principle for Molecular Phenotypes
New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.
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.