All Competitors

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

Generative foundation model for antibody and multispecific design, doubling its predecessor's experimental success rate on therapeutic targets.

Discrete diffusion model for conditional antibody sequence design with germline-absorbing noising that focuses learning on somatic variation.

464M-parameter structure prediction and design model that improves antibody-antigen complex accuracy over Protenix-v1 and adds generative VHH design.

Transformer that generates multi-species antibody and nanobody framework regions at the mRNA level, conditioned on input CDRs, across six species.

Contrastive antibody language model predicting antibody-antigen binding specificity from sequence with a dual-encoder, cross-attentive architecture.

Guided discrete diffusion model for antibody lead optimization, conditioning sequence design on the seed binder's CDR canonical backbone conformation.

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

Multimodal all-atom generative model for zero-shot de novo antibody and protein-binder design, validated by wet-lab hit rates from small batches.

Multimodal viral foundation model over nucleotide and protein sequence, built for virus discovery, function annotation, and antibody design.

Biochemistry-aware inverse folding model that augments backbone geometry with physicochemical point clouds, reaching ~90% sequence recovery on CATH.

Discrete generative model for antibody protein sequences combining MCMC walks on a smoothed energy landscape with one-step denoising jumps.

Structure-conditioned graph transformer trained with masked language modeling to learn residue encodings for inverse folding and antibody design.