All Competitors

T-cell receptor-MHC restriction prediction from amino acid sequence, mapping TCRs to their restricting HLA allele at 0.97 held-out AUC.

Reinforcement learning framework that fine-tunes the ProGen2-OAS antibody language model with GRPO to cut germline bias in generated sequences.

Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.

Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.

Masked language model for T-cell receptor and peptide-MHC binding prediction, with compositional pretraining and non-autoregressive decoding.

Single-cell latent diffusion model fine-tuned on 14.5 million CD4+ T cells to simulate transcriptomic effects of single-gene perturbations.

Structure-based conformational B-cell epitope predictor that scores local antigen surface patches with ESM-2 embeddings and an ensemble MLP.

Antibody language model that generates paired heavy and light variable domains, with a developability-conditioned variant for manufacturable designs.

Generative language model trained on 558 million antibody sequences for infilling-based design of CDR loops and full-length immunoglobulin sequences.

BERT-based antibody language model pretrained on 57M B cell receptor sequences for paratope prediction and convergent antibody discovery.

Antibody-specific language model trained on the OAS database for restoring missing residues and generating high-quality sequence representations.