All Competitors

Target-conditioned peptide binder design model that samples hot-spot residues from an energy-based density, then extends fragments autoregressively.

Macrocyclic peptide binder design against protein targets, cyclizing a diffusion backbone generator's positional encoding so it closes rings.

Lasso peptide language model that adapts ESM-2 to threaded RiPP core sequences, supplying embeddings for cyclase substrate and activity prediction.

Pocket-conditioned peptide designer: twin diffusion models generate an inhibitor backbone from receptor pocket geometry, then predict its sequence.

De novo peptide generation from four ProtGPT2 fine-tunes, one per design goal: hemolytic, non-hemolytic, non-fouling, and soluble sequences.

Blind peptide binder design from a protein sequence alone, evolving linear or cyclic binders against a frozen AlphaFold2 with no binding site given.

Full-atom peptide binder design against a target pocket, generating backbone frames, side-chain torsions and residue types in one joint flow.

All-atom peptide conformational sampling from sequence, using hypernetwork-conditioned diffusion trained by energy against a molecular force field.

Cyclic peptide structure prediction and de novo macrocycle design, by wrapping a frozen structure predictor's positional encoding into a ring.

In silico directed evolution that designs peptide binders against a chosen protein interface from sequence alone, scored by a frozen AlphaFold2.