All Competitors

Perturbation-trained single-cell foundation models (up to 3B parameters) that jointly model genes, cells, and compounds for precision oncology tasks.

Multimodal molecular large language model grounding molecule understanding and generation in fine-grained, multi-level chemical knowledge.

Structure prediction backbone that swaps AlphaFold3-style triangle attention for triangle multiplication, cutting compute without losing accuracy.

Molecular docking model that predicts protein-ligand binding poses with multi-stage Riemannian flow matching, yielding physically valid geometry.

Molecular graph transformer for ADMET property prediction, pretrained on 11M molecules under a joint SMILES-reconstruction and contrastive objective.

Sequence-only interaction model scoring protein-protein and protein-ligand pairs plus functional annotation across whole proteomes in seconds.

De novo ligand design framework that generates protein binders and small molecules by inverting gradients through a differentiable docking model.

Graph diffusion transformer for in-context molecular design, adapting to new tasks from a few molecule-property demonstrations without fine-tuning.

SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation, predicting binding affinity and confidence in the same network.

Geometric deep learning scoring function for protein-ligand binding affinity, pretrained on synthetic complexes and fine-tuned on PDBbind structures.

Discrete flow generative model over fragmented SMILES for de novo, fragment-constrained, and property-optimized small-molecule drug design.

Sequence-based protein-ligand binding site predictor pairing a protein language model with a SMILES chemical language model for zero-shot ligands.

Multimodal drug-target interaction model aligning SMILES, molecule text, taxonomy, and protein sequence with a Gramian volume contrastive objective.

Spectroscopy-grounded molecular foundation model that reads NMR, IR, and mass spectra as text, elucidating structures and generating 3D conformers.

Structure-free RNA-small molecule binder discovery model that predicts ligands and their binding sites from RNA sequence using an RNA language model.

SE(3)-equivariant chemical language model for pocket-based 3D molecule generation, used to design an HPK1 inhibitor with in vivo anti-tumor efficacy.

Multimodal scientific foundation model unifying protein, DNA/RNA, and small-molecule structure in one token vocabulary for cross-domain reasoning.

Chemical language model for fragment-based drug discovery, trained on all 62M ZINC-22 fragments. Samples 99.9% chemically valid fragment SMILES.

Protein language model that predicts which of eight lipid categories a protein binds from sequence alone, plus binding sites and mutation effects.

Discrete graph diffusion model for multi-property molecular generation, composing per-property score guidance over arbitrary condition subsets.

Protein-ligand binding site prediction that ranks pocket residues and pocket center coordinates, staying accurate on AlphaFold-predicted structures.

Sequence-based multitask model predicting covalently ligandable cysteines and reversible ligand-binding residues across the human proteome.

RNA small-molecule binding site prediction from sequence alone, pairing frozen RiNALMo embeddings with a lightweight MLP classifier.