All Competitors

Protein foundation model for de novo enzyme design that co-designs sequence and 3D structure under small-molecule ligand guidance, at 730M parameters.

Compact 167M-parameter protein language model built on a multiplicative LSTM, giving zero-shot variant effect and fitness prediction from sequence.

Protein-language diffusion model generating all-atom conformational ensembles for intrinsically disordered proteins and disordered regions.

Transformer that predicts protein-RNA binding affinity from Boltz-2 pre-structural embeddings via cross-modal attention, with no 3D structure step.

Autoregressive generative model for protein molecular dynamics that emits flexible-length trajectories frame by frame with anti-drifting sampling.

Multimodal reasoning LLM for protein function prediction, fusing protein language model embeddings to emit interpretable GO-term reasoning traces.

Protein function prediction model that autoregressively generates Gene Ontology terms from amino acid sequence instead of classifying fixed labels.

Contrastive dual-encoder model embedding protein domains and peptides in one space to predict domain-peptide binding specificity at proteome scale.

Structure-aware protein language model using structure-guided masking and a causal objective for variant effect prediction and protein discovery.

Generative foundation model that produces atom-level molecular dynamics trajectories for protein monomers and protein-ligand complexes.

Multimodal reverse-translation language model that generates species-aware mRNA coding sequences from protein sequences, conditioned on host taxonomy.

Protein language model that classifies RNA-binding proteins, localizes RNA-binding domains, and scores mutation effects at single-residue resolution.

Protein backbone design model pairing flow matching with a Mamba state-space backbone, generating long proteins in linear time with exact geometry.

Dual-encoder contrastive model that retrieves enzymes for query reactions by matching reaction fingerprints to protein sequence embeddings.

Predicts protein complex stoichiometry from amino acid sequence alone, ranking copy numbers in seconds and exporting AlphaFold3-ready JSON files.

Geometric deep learning model that learns atomic-scale representations of molecular interfaces across proteins, small molecules, and nucleic acids.

Transformer that generates multi-species antibody and nanobody framework regions at the mRNA level, conditioned on input CDRs, across six species.

Flow-matching generative model for de novo atomistic protein binder design against protein and small-molecule targets, including carbohydrate binders.

Sequence-to-ensemble predictor that generates conformational ensembles of intrinsically disordered proteins zero-shot, with no per-sequence refitting.

All-atom generative foundation model that designs small molecules, peptides, and nanobodies against a target binding site from a single checkpoint.

Edit-based flow-matching model that proposes protein variants by learning insertions, deletions, and substitutions on a template sequence.

Phosphopeptide detectability prediction for mass spectrometry, rescoring DDA identifications and pruning DIA spectral libraries to cut search time.

Paired-sequence protein language model that jointly encodes two interacting chains to predict interactions, binding affinity, and interface contacts.