All Competitors

Drug perturbation model predicting post-treatment gene expression from a cell's baseline profile and compound structure, zero-shot on unseen drugs.

Scientific multimodal foundation model, a 241B-parameter MoE with a tokenizer that reads molecular formulas and protein sequences natively.

Drug-drug interaction event text generation from two molecular structures, conditioned on biological functions selected for each drug in the pair.

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

Tandem mass spectrum prediction for intact N- and O-glycopeptides that localizes O-glycosylation sites from HCD spectra alone, without ETD.

Structure-based drug design model that generates 3D ligands inside a protein pocket by interpolating distribution parameters instead of samples.

Structure-based drug design model that inpaints a 3D ligand density into an empty protein pocket, then decodes those voxels into valid SMILES.

Synthesizable 3D molecule generation that jointly samples building blocks, reactions, and atomic coordinates, returning a synthesis route per design.

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

Conditional chemical language model prompted with a protein target and mechanism of action to score and design molecules without structural input.

Antimicrobial discovery model predicting compound potency against unseen bacterial strains and generating de novo antibiotics from pathogen genomes.

Site-specific structure prediction conditioning AlphaFold3 diffusion on a fixed receptor and known binding pocket. 81.2% success on PoseBusters V2.

Structure-based 3D molecule generation with one diffusion backbone for fragment growing, linker design, scaffold hopping, and side-chain decoration.

Single-cell perturbation foundation model predicting transcriptomic responses to CRISPR and small-molecule interventions in cancer cells.

All-atom biomolecular structure prediction with adapters for allosteric states, user-defined interface constraints, and binding affinity.

Molecular conformer generation from 2D graphs with a diffusion transformer that replaces equivariant layers with graph shortest-path attention biases.

Tandem mass spectrometry model that embeds MS/MS spectra and molecular graphs in one space, ranking candidate structures without a spectral library.

Structure-free ligand generation conditioned on a protein sequence alone, using masked diffusion over SMILES trained on 1.2M BindingDB active pairs.

Multimodal drug-response model coupling cell and molecule foundation models, pretrained on 1.8M perturbation RNA-seq profiles over 22,000 compounds.

Molecular property prediction foundation model pretrained on computed descriptors. Wins 75% of Polaris tasks and 97% of MoleculeACE assays.

Structure-based drug design model pairing SE(3)-equivariant diffusion with retrieval of pocket-matched scaffolds to generate ligands for a target.

Graph-level self-supervised pretraining for 3D molecules, reconstructing whole-molecule geometry to sharpen quantum property and force prediction.

Ligand-binding protein design driven by a natural-language function description plus a ligand SMILES string, in 1B and 3B parameter variants.