Labs & Groups (2)
Baker Lab
A computational protein design lab at the University of Washington, creating software and new proteins for challenges in medicine and sustainability.
1 model
Institute for Protein Design
A University of Washington institute creating proteins from scratch, with computational design methods for therapeutics, vaccines, and nanomaterials.
7 models
Models (14)
Gene regulation model that conditions a pretrained DNA sequence embedding on CpG methylation to capture cell-type and allele-specific regulation.
Multi-sequence spine MRI foundation model with DINOv3 encoders, supporting condition classification, pathology localization, and report generation.
Beta-Barrel Nanopore Design Model
Institute for Protein Design / University of Washington
Released June 4, 2026
Diffusion-based backbone generation and sequence design method for programmable asymmetric transmembrane beta-barrel nanopores.
Discrete diffusion model for conditional antibody sequence design with germline-absorbing noising that focuses learning on somatic variation.
All-atom protein design diffusion model conditioned on ligands, nucleic acids, and other non-protein atoms, supporting enzyme and DNA binder design.
Atom-level diffusion model for de novo enzyme design that scaffolds arbitrary active-site geometries without specifying catalytic residue positions.
Gene expression prediction model combining DNA sequence with Hi-C contact maps to capture 3D chromatin looping behind cell-type-specific expression.
Multimodal foundation model that distills Evo 2 into a compact encoder guided by Hi-C data, predicting cell-type-specific 3D genome architecture.
GMAI-VL-R1
Shanghai AI Laboratory / Fuzhou University / Shanghai Innovation Institute / Fudan University / Monash University / University of Washington / Stanford University
Released April 2, 2025
General medical vision-language model trained with reinforcement learning to reason step by step over medical images for diagnosis and visual QA.
Protein sequence design model that represents small molecules, nucleotides, and metals at atomic resolution, enabling ligand-aware enzyme design.
Deep network that predicts structures of full biological assemblies: proteins, nucleic acids, small molecules, metals, and covalent modifications.
De novo protein design diffusion model that generates backbone structures conditioned on binding targets, symmetry constraints, and functional motifs.
AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.
Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.