All Competitors

Pocket-based molecular docking with an SE(3)-equivariant diffusion transformer. Places 77.65% of top-1 poses within 2 Å RMSD on PoseBusters.

Flexible protein-ligand docking and binding affinity prediction from an apo receptor structure and ligand SMILES, using an 8-layer pair transformer.

Protein binder design that inverts the frozen Boltz-1 all-atom predictor, targeting small molecules, nucleic acids, metals, and modified residues.

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

Open therapeutics foundation models from Google, built on Gemma-2, for drug-discovery property prediction and conversational reasoning.

Ames mutagenicity prediction conditioned on bacterial tester strain and S9 metabolic activation, holding sensitivity on chemically novel compounds.

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

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

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.

Small-molecule foundation model pairing a graph encoder with a Transformer SMILES decoder so compounds can be optimized directly in encoding space.

Synthesis planning model that generates full synthetic routes from Enamine building blocks and reaction templates for any target small molecule.

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

Natural product chemistry prediction from biosynthetic gene clusters, assigning ChemOnt ontology classes from the cluster's Pfam domain composition.

Enzyme turnover number (kcat) prediction from sequence and substrate SMILES, scaling to genome-wide kinetic parameters for metabolic modeling.

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

Structure-based drug design framework that scores interaction-aware fragments against protein subpockets, then diffuses a 3D scaffold to link them.

Generative foundation model that co-generates sequence and 3D coordinates for proteins, small molecules, and crystals under functional objectives.

De novo small molecule generation that assembles drug-like graphs atom by atom, pretrained on cheap property proxies and finetuned per objective.

Cross-domain molecular foundation model encoding small molecules, protein pockets, and their complexes in 2D and 3D on one Transformer backbone.

Full-atom flow matching model that generates a ligand and the induced-fit holo pocket together, starting from an apo binding site.

Latent diffusion model generating 3D drug-like molecules and inorganic crystals from one shared all-atom autoencoder and Transformer denoiser.

Drug-combination safety prediction that fuses molecular structure, pathway knowledge, cell viability, and transcriptomic response to perturbation.