Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions.

ABSTRACT: Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels a

SUBMITTER: Hansson FG 

PROVIDER: S-EPMC12048553 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature