MIT CSAIL

Open model that jointly predicts biomolecular structure and small-molecule binding affinity, approaching FEP+ accuracy in seconds on a single GPU.

Interactive foundation model for biomedical image segmentation, prompted with scribbles, clicks, and bounding boxes to segment unseen structures.

A foundational keypoint model for robust, flexible brain MRI registration, pretrained on over 100,000 3D volumes and supporting rigid, affine, and deformable alignment.

A rigorous benchmarking study of scBERT and scGPT for cell type annotation, comparing foundation models against logistic regression baselines.

An in-context learning model that segments unseen medical imaging tasks from a few labeled examples, with no retraining or fine-tuning.