SPECTRA
Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets
UniTS
Unified Time Series Model that Can Process Various Tasks Across Multiple Domains with Shared Parameters and Does Not Have any Task-Specific Modules
PDGrapher
Combinatorial Prediction of Therapeutic Perturbations Using Causally-Inspired Neural Networks
TxGNN
Zero-Shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician Centered Design
PINNACLE
Contextualizing Protein Representations Using Deep Learning on Interactomes and Single-Cell Experiments
TimeX
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Raincoat
Domain Adaptation for Time Series Under Feature and Label Shifts
SHEPHERD
Deep Learning for Diagnosing Patients with Rare Genetic Diseases
metapaths
Similarity Search in Heterogeneous Knowledge Graphs via Meta Paths
Mutual Interactors
Phenotype Discovery in Molecular Interaction Networks
Raindrop
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Therapeutics Data Commons
Machine Learning Datasets and Tasks for Drug Discovery and Development
DeepPurpose
Deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening
Graph Query Embeddings
Method for embedding logical queries on knowledge graphs
Network-Guided Matrix Completion
Method for probabilistic prediction and imputation of interactions using prior knowledge
Multi-BioNER
Deep multi-task learning for cross-type biomedical named entity recognition
Latest News
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.
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