All Competitors

Protein conformational ensemble generator conditioned on backbone geometry alone, sampling MD-like dynamics without MSAs or a folding model.

Protein language models evotuned on influenza A hemagglutinin, with a pLM entropy metric scoring per-site conservation from a single input sequence.

Antibody-aware B-cell epitope prediction from a graph convolutional network over frozen antibody and antigen protein language model embeddings.

Fixed-backbone protein sequence design that co-generates amino acid identity and sidechain conformation, with 49.7% sequence recovery on CATH 4.2.

Protein function annotation that reshapes language model embeddings with a neural-collapse loss so rare EC, Pfam, and GO classes stay separable.

MSA-free structure prediction for TCR-peptide-MHC complexes, pairing a protein-protein-interaction language model with a flexible docking module.

Enzyme kcat and KM prediction from sequence and substrate SMILES, binned by order of magnitude so catalytic-site mutations shift the prediction.

Disordered protein ensemble prediction from sequence, generating hundreds of conformers in seconds via latent diffusion over distance maps.

Antibody chain pairing model that scores whether a heavy and a light chain are cognate partners, fine-tuned from an antibody-specific language model.

Antibody affinity maturation framework that steers flow-matching structure generation with a binding predictor, then mutates CDRs by inverse folding.

N-linked glycosylation site prediction stacking SVM, XGBoost, and KNN classifiers over ProtT5, ESM-2, and ProteinBERT sequence embeddings.

De novo protein sequence design conditioned on target vibrational dynamics, pairing a diffusion designer with a predictor that ranks candidates.

Absolute protein folding stability prediction that estimates ΔG by jointly modeling the folded and unfolded ensembles as residue-pair distograms.

Neural ODE model of protein network dynamics, pretrained on 38 million perturbed protein measurements for drug efficacy and synergy prediction.

Unified science foundation model treating molecules, proteins, RNA, DNA, and materials as one sequence language, in 1B, 8B, and 46.7B sizes.

Antibody and TCR CDR sequence design by structure retrieval, matching query loops against solved CDR structures rather than generating residues.

Multi-omics foundation model that folds DNA, RNA, and protein into one codon-level nucleotide representation following the central dogma.

Protein-conditioned RNA sequence and structure co-design, refining flow-matched backbones against Lennard-Jones and folding free-energy terms.

Binding free energy change prediction for antibody mutations, from a lightweight transformer that scores 10,000 variants in under five minutes.

Protein cleavage site prediction that generalizes to proteolytic enzymes unseen in training by encoding active-site chemistry alongside sequence.

Sparse autoencoders on the ESM-2 residual stream that expose interpretable protein features, with an open visualizer for what each latent detects.

Tertiary structure-based RNA design model that fuses RNA backbone geometry with protein language model features of the bound partner protein.

Kinase-inhibitor binding affinity prediction fusing a contrastively pretrained molecular graph encoder with structure-informed kinase embeddings.