All Competitors

Variable-length generative protein design across structure, sequence, motif scaffolding, and peptide co-design via a generalized Poisson flow.

Generative model for chemically modified and macrocyclic peptides that builds molecules in HELM notation, supporting de novo design and infilling.

Sequence-based discrete-diffusion framework that designs peptide binders with specified agonist or antagonist behavior against GPCR targets.

Diffusion generative model for structure-based peptide inverse folding, pairing a geometric GNN encoder with a Transformer denoiser.

Latent diffusion model that designs D-peptide binders against native L-protein targets, generalizing across chirality via axial vector features.

Zero-shot peptide binder designer that runs diffusion in a pretrained protein embedding space, proposing binders without structure prediction.

Sequence-only latent diffusion model that designs target-specific peptide binders, cascaded with an affinity classifier through joint optimization.

Flow-matching model for therapeutic peptide design that co-designs sequence, structure, and molecular surface to disrupt protein-protein interactions.

Denoising diffusion bridge model for peptide binder design that generates ligand surfaces and backbones complementary to a target receptor surface.

De novo antibiotic design framework coupling a 6.4B-parameter protein language model with reinforcement learning to generate antimicrobial peptides.

Structure-free peptide binder design conditioned only on a target protein sequence, using contrastive alignment to steer a latent diffusion model.

Flow matching model that builds any non-canonical amino acid into a protein pocket from its SMILES string, at 1.43 Å mean RMSD on held-out ncAAs.

De novo design of heavy metal-binding peptides by classifier-guided diffusion over ESM-2 embeddings, with Cu and Zn binders validated in vitro.

Cyclic peptide design conditioned on target protein structure, generating all four cyclization types via all-atom, all-bond harmonic SDE modeling.

Protein sequence design by flow matching in a compressed language-model latent space, spanning peptides, antibodies, and antimicrobial peptides.

Antimicrobial peptide platform whose GPT-style generator is conditioned on E. coli or S. aureus activity, then filtered for potency and hemolysis.

Cyclic peptide binder design by Monte Carlo tree search over sequence space, scored by predicted confidence of the peptide-target complex fold.

All-atom generative foundation model for biomolecular structure, unifying protein-ligand docking, structure-based drug design, and peptide design.

Generative design of protease substrates, producing 10-mer peptides conditioned on a target cleavage profile across 18 matrix metalloproteinases.

Flow matching model for de novo 3D peptide design that converges peptide position before conformation, mirroring the physical order of docking.

Liquid-liquid phase separation predictor that scores proteins and residues from sequence and designs phase-separating peptides by gradient descent.

Cyclic peptide structure prediction for sequences carrying unnatural amino acids, adding atom-level features and cyclization-aware position encoding.

Peptide binder design by mimicking the binding interface of a known receptor or antibody, generating all-atom peptides through latent diffusion.