All Competitors

Structure-based RNA virtual screening that scores small molecules against a binding site's base-pairing graph, around 10,000x faster than docking.

Encoder-decoder framework unifying molecules, proteins, and natural language with SELFIES notation for cross-modal drug discovery tasks.

Conditional GAN that generates small molecules against a protein-protein interaction interface, encoding the complex with graph attention networks.

De novo ligand design from a target protein sequence, writing SMILES autoregressively over a merged protein-ligand BPE vocabulary.

Equivariant heterogeneous graph network that rescores docked protein-ligand poses for virtual screening and ranks structural analogs by activity.

Multi-modal LLM answering free-form questions about a compound's indications, pharmacodynamics and mechanism of action from its SMILES string.

Drug pair synergy prediction for rare cancer tissues, read from a language model's representation of a screening row written out as a sentence.

Structure-based drug design by SE(3)-equivariant diffusion over 3D atom coordinates and types, with the same frozen network scoring binding affinity.

Blind protein-ligand docking by diffusion over ligand translations, rotations and torsion angles, reaching 38% top-1 accuracy on PDBBind.

Large-scale chemical language model trained on 1.1 billion SMILES strings using linear attention transformers for molecular property prediction.

Small-molecule generation over SMILES through a fixed-size latent space dense enough that gradient-free search over it optimizes chemical properties.

Structure accuracy estimation over atom graphs, scoring macrocyclic peptide and protein-DNA models that residue-level predictors cannot represent.

SMILES language model pretrained on 100M molecules, transferring to forward reaction prediction, retrosynthesis, molecular optimisation, and QSAR.

Metabolite-likeness scoring ranks any chemical structure by its distance from a learned hypersphere of known endogenous metabolites.

Chemical language model for small-molecule drug discovery, embedding SMILES for property prediction and sampling new molecules from its latent space.

Drug repurposing model that predicts a compound's L1000 transcriptional signature from SMILES and ranks it against a disease gene signature.

Self-supervised molecular graph model contrastively pretrained on ~10M unlabeled PubChem molecules, then fine-tuned for property prediction.

Chemical language model pretrained on up to 77 million PubChem SMILES strings for molecular property prediction on the MoleculeNet benchmarks.

RNA virtual screening that reads a binding site's base-pairing graph and predicts the chemical fingerprint of its ligand to rank compound libraries.

Molecular graph transformer pretrained on 11 million unlabelled compounds, used as a frozen fingerprint source or fine-tuned for property prediction.

Molecular descriptor model that embeds compounds into a continuous 512-dimensional space by translating between equivalent chemical line notations.