Common Interface Network for Multi-domain Biomolecular Interaction Learning

Biomolecular interactions underlie core cellular processes and modern drug discovery, yet reliable prediction across interaction types remains limited by the lack of universal interface representations that transfer across molecular domains. Here, we proposed the Common Interface Network (ComIN), a framework that learns interface representations via contrastive learning on interface atom graphs. By training jointly on protein–protein, protein–peptide, and protein–small molecular interactions, ComIN synthesizes a unified embedding space that demonstrates superior performance over existing domain-specific models. Extensive validation across five distinct interaction-centric tasks in drug discovery and immune recognition underscores the broad transferability and robustness of ComIN as a representational tool. Leveraging this universality, we built ComINdex, a cross-domain search engine indexing million-scale interfaces with ComIN-generated representations to enable efficient retrieval in support of scalable biomolecular function analysis and design.