Learning Universal Representations of Intermolecular Interactions with ATOMICA.

ABSTRACT: Molecular interactions underlie nearly all biological processes, but most machine learning models treat molecules in isolation or specialize in a single type of interaction, such as protein-ligand or protein-protein binding. Here, we introduce ATOMICA, a geometric deep learning model that learns atomic-scale representations of intermolecular interfaces across five modalities, including proteins, small molecules, metal ions, lipids, and nucleic acids. ATOMICA is trained on 2,037,972 interaction complexes using self-supervised denoising and masking to generate embeddings of interaction interfaces at the levels of atoms, chemical blocks, and molecular interfaces. ATOMICA's latent space is compositional and captures physicochemical features shared across molecular classes, enabling representat

SUBMITTER: Fang A 

PROVIDER: S-EPMC12026499 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature