University of Texas at Austin

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

Cell world model pretrained on a 2.4M-cell mouse embryonic atlas, predicting one-step transcriptional state transitions and perturbation response.

Chemical language models pretrained on SMILES for therapeutic peptides, natively representing non-canonical residues, cyclization, and conjugation.

Structure prediction backbone that swaps AlphaFold3-style triangle attention for triangle multiplication, cutting compute without losing accuracy.

Distilled few-step protein backbone generator that adapts Score Identity Distillation to Proteina for over 20x faster de novo structure sampling.

Self-supervised vision foundation model pretrained on 1.3M medical images via second-order graph matching, for segmentation and classification.