MIT

Unified all-atom generative model for biomolecular structure prediction, binder filtering, and controllable protein and nanobody design.

Flow-matching framework that translates omics signatures across biological domains, such as mouse to human transcriptomics, without paired samples.

Multimodal foundation model integrating spatial transcriptomics, H&E histopathology, and pathway scores for single-cell niche discovery.

Cell-centric microscopy foundation model that distills morphology and microenvironment views into a unified embedding for virtual spatial omics.

Energy-based model of protein conformational space, turning a diffusion model into a statistical potential for structure ranking and mutation scoring.

All-atom generative model for de novo protein and peptide binder design against diverse biomolecular targets, wet-lab validated across 26 targets.

Graph diffusion transformer for in-context molecular design, adapting to new tasks from a few molecule-property demonstrations without fine-tuning.

Single-cell perturbation-response model that predicts transcriptomic and imaging outcomes of unseen genetic perturbations via a VAE with attention.

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

Multimodal clinical foundation model reasoning jointly over 2D and 3D medical images, ECG time-series, and text reports across nine clinical domains.

Self-supervised transformer pretrained on millions of tandem mass spectra, giving embeddings for spectral annotation and fingerprint prediction.

Motion foundation model for wearable accelerometry, trained with relative contrastive learning on 1B segments from 87,376 participants.

Open-source structure prediction model for proteins, nucleic acids, and small molecules, trained on public data to AlphaFold3-level accuracy.

Mixed-modality genomic language model encoding protein coding sequences as amino acids and intergenic DNA as nucleotides in native genomic context.

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

Keypoint-based foundation model for brain MRI registration, pretrained on over 100,000 3D volumes for rigid, affine, and deformable alignment.

Genomic language model trained on metagenomic scaffolds that learns protein co-regulation and function by modeling gene context and operon structure.

Protein conformational ensemble generator that fine-tunes AlphaFold 2 with flow matching, sampling protein dynamics beyond a single static structure.

Medical image segmentation model that solves unseen segmentation tasks in context from a few labeled examples, with no retraining or fine-tuning.