All Competitors

Virtual drug screening from per-atom protein and ligand embeddings retrieved by nearest neighbors. 30.4 EF1% on DUD-E at ~14 s per million molecules.

Structure-based virtual screening model that scores ligands against apo and predicted pockets, lifting blind-apo EF1% on DUD-E from 11.75 to 37.19.

Relative protein-ligand binding affinity prediction from docked complexes, matching Schrodinger FEP+ ranking accuracy zero-shot on the FEP benchmark.

Cross-modal co-embedding of biosynthetic gene clusters and natural products, enabling bidirectional retrieval between gene cluster and compound.

Protein-ligand docking model for high-throughput virtual screening, predicting binding poses with graph neural networks at low computational cost.

Molecular embedding model that turns SMILES into SE(3)-invariant vectors for property prediction, similarity search, and compound clustering.

Instruction-tuned LLM series for multi-property molecule optimization that improves named drug properties while preserving those already in range.

Cyclic peptide design conditioned on target protein structure, generating all four cyclization types via all-atom, all-bond harmonic SDE modeling.

Protein-ligand binding affinity model that tokenizes quantum electron-cloud density into discrete codes, plus a distilled cloud-free variant.

Structure-constrained molecular generation using reinforcement learning over reaction templates, trained without any external property metric.

Self-supervised transformer pretrained on millions of tandem mass spectra, giving embeddings for spectral annotation and fingerprint prediction.

Template-guided 3D ligand pose generation by flow matching plus differentiable refinement, placing 77.8% of poses within 2 Å RMSD on AlignDockBench.

Molecular dynamics emulator generating time-coarsened trajectories for small molecules, peptides, and proteins from one shared atomic representation.

Structure-based drug design by diffusing medicinal-chemistry fragments into a binding pocket, yielding synthesizable, selective, drug-like molecules.

Pharmacophore-conditioned diffusion model generating 3D molecular graphs that satisfy a given pharmacophore hypothesis without a target structure.

Ligand-aware protein language model that cross-attends SaProt embeddings to ligand SMILES, beating SaProt across six downstream benchmarks.

Molecular conformation description language encoding 3D geometry as SMILES plus internal-coordinate tokens, turning 3D modeling into a sequence task.

Structure-based drug design that schedules noise separately for 3D coordinates and 2D topology, reaching a 95.9% PoseBusters valid rate on CrossDock.

Pocket-conditioned 3D diffusion model for scaffold decoration, guided by evolutionary residue conservation and a protein-ligand interaction prior.

Retrosynthesis and reaction prediction LLM that jointly learns molecular fragmentation and recombination from a 4.4M-instruction chemistry corpus.

Generative diffusion model that samples biomolecular conformational ensembles for proteins, RNA, and ligands in hours instead of millisecond-scale MD.

Physics-guided all-atom diffusion model for protein-ligand complex prediction, reaching 95.3% success on PoseBusters redocking with a known pocket.

Cryptic protein binding site prediction from sequence, backed by a database of 5,151 cryptic sites mined from 6 million apo-holo PDB alignments.