All Competitors

Protein-ligand binding affinity scoring model for virtual screening that generalizes to novel pockets and ligands under strict train-test splits.

Preparation-free protein-ligand binding affinity prediction from a protein sequence and a ligand SMILES, using a cascaded mixture-of-experts fusion.

Protein-ligand binding affinity prediction from sequence and SMILES, without MSAs. Coarse-grained cofolding runs over 10x faster than Boltz-2.

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

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

Graph-transformer foundation model pretrained on 3M protein pockets and 5M molecules as E(3)-equivariant graphs for protein-ligand representation.

Unified drug design engine for protein-ligand structure prediction, binding affinity estimation, and compound generation from Isomorphic Labs.

Protein-ligand scoring function that conditions probabilistic geometric potentials on language model priors to rank docked poses and binding affinity.

Peptide developability predictor scoring solubility, permeability, toxicity, and binding from amino-acid sequences or chemically modified SMILES.

Paired heavy/light antibody language model fine-tuning ESM-2 and ESM-C with CDR-preferential masking for zero-shot binding affinity embeddings.

SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation, predicting binding affinity and confidence in the same network.

Geometric deep learning scoring function for protein-ligand binding affinity, pretrained on synthetic complexes and fine-tuned on PDBbind structures.

Protein-protein binding affinity prediction from sequence alone, pairing frozen protein language model embeddings with gradient-boosted trees.

Multimodal tokenizer for antibody CDR loops, encoding backbone dihedrals and sequence as discrete tokens that plug into antibody language models.

RNA-small molecule binding affinity prediction from RNA sequence and compound SMILES, pairing a 56M-parameter RNA language model with ChemBERTa-2.

Binding free-energy surrogate trained on 1.4M molecular dynamics frames, ranking docking poses ~28,000x faster than physics-based MMGBSA.

Protein-ligand interaction predictor that types seven contact classes between residues and ligand functional groups from sequence and SMILES alone.

Antibody-antigen binding free energy predictor fusing ESM-2 sequence embeddings with persistent homology and interface geometry via cross-attention.

Protein-ligand binding model that embeds ligands, pockets, and sequences in hyperbolic space, unifying virtual screening and affinity ranking.

Protein-ligand interaction model pretrained on solvent-aware conformer ensembles, reaching 97.1% AUC on DUD-E virtual screening.

Binding free energy change (ΔΔG) predictor for protein-protein interfaces, decomposing mutational effects into inverse-folding and energy-model terms.

Protein surface tokenizer that turns surface-exposed residues into codebook tokens, lifting SKEMPI binding affinity change prediction to r = 0.600.

Protein-ligand binding affinity and mutation ΔΔG predictor fusing residue, ligand, and interaction graphs, evaluated on leak-proof LP-PDBBind splits.

Scoring function for protein, nucleic acid, and small-molecule complexes that predicts binding affinity, ranks docked poses, and screens ligands.