All Competitors

A ~100M-parameter autoregressive protein-binder generator using a sparse Mixture-of-Experts architecture for sequence-only, receptor-conditioned binder design without 3D structure inputs.

Partially latent flow-matching generative model for de novo atomistic protein binder design against protein and small-molecule targets, with experimental validation at million-design scale.

Latent diffusion model that designs D-peptide (mirror-image) binders against native L-protein targets via cross-chirality generalization, with wet-lab validation.

A Pairformer-based flow-matching generative model for de novo protein binder backbone design, paired with in silico maturation to reach picomolar-to-nanomolar affinities.