All Competitors

All-atom foundation model for macrocyclic peptide structure prediction, permeability estimation, and de novo design across non-canonical chemistries.

Generative language model that designs drug-like SMILES conditioned on disease ontology and a target protein sequence for de novo drug discovery.

Open-source all-atom co-folding foundation model for protein-ligand, protein-protein, and antibody-antigen complex prediction in drug discovery.

Vision-language model that reads molecular structure images, translating them to SMILES, captions, and properties via chemical-bond topology.

Structure-based drug design language model fusing protein structural and evolutionary encoders with SAFE fragment tokens for hit-to-lead generation.

Physics-informed graph neural network predicting metabolite concentrations from gene expression, generalizing zero-shot to unseen metabolites.

Heterograph foundation model for metabolic engineering, fusing protein, chemistry, and text embeddings over a 6.9M-node biological knowledge graph.

Diffusion model for 3D small-molecule design against protein-protein interaction sites, guided by the natural binding peptide or protein partner.

Contrastive multimodal model for perturbation screens, aligning transcriptomic signatures with text and cell-painting image embeddings.

Molecular foundation model that turns SMILES into 2048-dimensional embeddings from multiple 3D conformations for ADMET and virtual screening.

Multimodal molecular generation model for drug design, conditioned on properties, pharmacophores, protein sequences, or protein binding pockets.

Diffusion model that generates 3D small molecules conditioned on protein pockets and partial fragments encoded as continuous spatial density maps.

Zero-shot generative framework that turns 3D pharmacophores into synthesis-ready DNA-encoded libraries of purchasable building blocks.

Decoder-only foundation model that unifies sequences, 3D structures, and natural language for small molecules and proteins in one shared token space.

NMR foundation model trained on 158 million simulated 1H and 13C spectra, transferring simulation-learned representations to real experimental data.

Small-molecule hit-discovery pipeline using Boltz-2 co-folding and affinity prediction to rank in-stock compounds or make-on-demand chemical space.

Toxicity screening foundation model that encodes Tox21 concentration-response curves and assay metadata into reusable 768-dimensional embeddings.

Perturbation-response predictor built on screen-level summary statistics, inferring unmeasured drug and genetic responses in new cellular contexts.

Specificity foundation model predicting small-molecule drug-target binding from sequence, scored as cross-modal retrieval without docking or assays.

Cryo-EM ligand modeling pipeline that detects bound ligand densities in a map, then reconstructs their atomic structures with a diffusion model.

Generative model for chemically modified and macrocyclic peptides that builds molecules in HELM notation, supporting de novo design and infilling.

Signed heterogeneous graph foundation model over the SIGMA-KG knowledge graph, predicting drug mode of action and drug-drug interactions zero-shot.

Motif-aware graph diffusion model for controllable molecular generation that adapts to unseen properties by learning a lightweight task embedding.

Chemical language model that generates matched molecular pair transformations from SMILES and SMARTS to design medicinal-chemistry analogs.