All Competitors

De novo protein design from natural language: a 16B-parameter framework turning text descriptions into sequences via structure-conditioned generation.

Retrieval-augmented protein language model that conditions ESM-2 on homologous sequences via cross-attention for conditional sequence generation.

Protein function prediction fusing five Gene Ontology pipelines, two of them deep models over protein and DNA language model embeddings.

Sequence-only peptide toxicity prediction, pairing a fine-tuned ESM-2 backbone with a bidirectional LSTM and focal loss for rare toxic peptides.

Protein function prediction that assigns Gene Ontology terms from predicted 3D structure, ESM-2 embeddings, and cross-species network propagation.

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

Structure-based drug design model pairing an autoregressive transformer for ligand graphs with a diffusion head for 3D binding-pose coordinates.

De novo binder design across small molecules, peptides, and antibodies from one geometric latent diffusion model over graphs of molecular blocks.

Antibody sequence generation model that samples paired human VH/VL chains, covering inpainting, inverse folding, and CDR grafting in one network.

De novo peptide sequencing from mirror-protease mass spectra, reading paired complementary spectra to recover near-complete fragment ion coverage.

Protein sequence representation model pairing a sequence autoencoder with a denoising diffusion model over its latent space for frozen embeddings.

Transferable coarse-grained force field for molecular dynamics of proteins, RNA, and lipids, built on the MACE equivariant graph architecture.

Protein language model pretrained on over nine billion sequences, giving residue embeddings and zero-shot single-site mutant scores from its logits.

Diffusion model that locates zinc binding sites in protein structures at 94% precision, without needing the number of ions specified.

Text-to-protein generative model designing de novo sequences from plain-language function descriptions, with designs confirmed by crystallography.

GPCR peptide agonist screening with a graph neural network over AlphaFold-Multimer active-state complexes and interatomic contact graphs.

Chemical language model family for target-aware ligand generation, conditioning molecule design on protein embeddings from a companion protein model.

Autoregressive 3D structure model built on an octree tokenizer, spanning molecule generation, molecular docking, and protein pocket prediction.

Protein conformational motion prediction from a single structure, using an SE(3)-equivariant GNN trained on ensembles mined from the PDB.

Protein dynamics model predicting per-residue probability of microsecond-millisecond conformational exchange from sequence or structure.

Predicts EC, GO, InterPro, Gene3D, keyword and cofactor terms from sequence, emitting database identifiers rather than free-text function guesses.

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

Binding affinity scoring for protein-ligand and lipid-protein pairs without a docked pose, used to rank the protein corona on candidate liposomes.