All Competitors

Discrete diffusion model for conditional antibody sequence generation that restricts learning to somatic variation via a germline-absorbing noising process.

Enhanced 464M-parameter version of Protenix with substantial gains in antibody-antigen complex prediction over v1, plus target-conditioned VHH-Fc generative design with up to 48% hit rates.

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 that predicts antibody-antigen binding specificity directly from amino acid sequence using a dual-encoder, cross-attentive architecture.

A multi-modal viral foundation model trained on 25.4B nucleotide and amino-acid tokens spanning nearly all known viruses, for virus discovery, function annotation, and antibody design.

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

An equivariant graph transformer trained with masked language modeling on protein structure to learn contextual amino acid encodings for sequence design and interface modeling.