Learning a CoNCISE language for small-molecule binding

Abstract

Rapid advances in deep learning have improved in silico methods for drug-target interaction (DTI) prediction. However, current methods do not scale to the massive catalogs that list millions or billions of commercially-available small molecules. Here, we introduce CoNCISE, a method that accelerates drug-target interaction (DTI) prediction by 2-3 orders of magnitude while maintaining high accuracy. CoNCISE uses a novel vector-quantized codebook approach and a residual-learning based training of hierarchical codes. Strikingly, we find that much of binding-specificity information in the small molecule space can be compressed into just 15 bits of information per compound, characterizing all small molecules into 32,768 hierarchically-organized binding categories. Our DTI architecture, which combines these compact ligand representations with fixed-length protein embeddings in a cross-attention framework, achieves state-of-the-art prediction accuracy at unprecedented speed. We demonstrate CoNCISEs practical utility by indexing 6.4 billion ligands in the Enamine dataset, enabling researchers to query vast chemical libraries against a protein target in seconds. A "CoNCISE + docking" pipeline screened Enamine to propose strong binders (predicted KD {approx} 10-20 {micro}M) of three difficult-to-drug targets, each within two hours. CoNCISEs advance could democratize access to largescale computational drug discovery, potentially enabling rapid identification of promising molecules for therapeutic targets and cellular perturbations.