All Competitors

De novo cyclic peptide binder design against a protein target, chaining a cyclized diffusion sampler, sequence design and structure prediction.

Antimicrobial peptide optimization framework pairing a transformer VAE latent space with constrained Bayesian optimization against an MIC oracle.

GPCR-peptide complex structure prediction conditioned on active or inactive receptor states, used to rank designed peptide agonists and antagonists.

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

De novo peptide design across non-canonical amino acid space, using guided diffusion over receptor-ligand interfaces to reach D-amino acid chemistry.

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

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.

Molecular docking and design foundation model that unifies structure-based drug design and peptide design at the atom level in one checkpoint.

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

Antimicrobial peptide generator running denoising diffusion in the continuous ESM-2 embedding space, validated in mouse infection models.

Peptide language model that generates antimicrobial, anticancer, and target-binding sequences, adapted per task by Mixture-of-Experts plugins.

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