Small molecule Models
Small-molecule foundation models learn representations of chemical structure from large libraries of molecules, supporting property prediction, de novo generation, and drug–target interaction modeling. By capturing the grammar of chemistry — bonds, scaffolds, and physicochemical properties — these models accelerate hit discovery, lead optimization, and the design of novel compounds. They underpin modern computational drug discovery, bridging molecular representation and the biological targets these molecules act on.
9 models in this category
Notable Models
Top-rated small molecule models from our evaluations
A signed heterogeneous graph foundation model pretrained on the SIGMA-KG knowledge graph for zero-shot drug mode-of-action, clinical response, and drug-drug interaction prediction.
A motif-aware graph diffusion transformer for controllable molecular generation that transfers to unseen properties by learning only lightweight task embeddings with the generator frozen.
Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.
Unified science foundation model from Microsoft Research treating molecules, proteins, RNA, DNA, and materials as a shared sequence language for cross-domain generation.
Multi-modal, multi-task biological foundation model trained on 2 billion samples spanning proteins, small molecules, and single-cell gene expression.
Multi-view molecular foundation model that integrates graph, image, and text representations via late fusion for molecular property and target prediction.