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.
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.
Excited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.
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.
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.
New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.
New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.
New paper with NASA in Nature Machine Intelligence on biological research and self-driving labs in deep space supported by artificial intelligence.
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.
Our approach evaluating explainability of geometric deep learning models is published in Scientific Data. Project website.
Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.
New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.
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.
New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.
Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.
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.
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.
We are excited to host the AI4Science meeting at NeurIPS discussing AI-driven scientific discovery, implementation and verification of AI in science, the influence AI has on the conduct of science, and more.
Check out our half-day tutorial with resources on methods and applications in graph representation learning for precision medicine.
Welcoming a research fellow Julia Balla and three Summer students, Nicholas Ho, Satvik Tripathi, and Isuru Herath.
Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.
Excited to welcome George Dasoulas and Huan He, new postdocs joining us this Summer.
Congratulations to George Dasoulas, our incoming postdoctoral fellow, on being named the 2022 Wojcicki Troper HDSI Postdoctoral Fellow. We are delighted to welcome George in our group.
Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.
Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!
Owen has been selected to present our research on explainable biomedical AI to members of the US Congress at the “Posters on the Hill” symposium. Congrats Owen!