A Heterogeneous Graph Framework for Inference of Metabolite–Protein-Drug Interaction Networks

Metabolite–protein interactions (MPIs) are essential for coordinating cellular metabolism and signaling. Yet, MPIs remain incompletely characterized due to the limited scalability of experimental methods and the complexity of tissue-specific regulatory mechanisms. Existing computational approaches often focus on direct interactions and overlook higher-order associations and effect of drug perturbations. Here we introduce TopoMPI, a graph-based framework that integrates five types of biological relationships—metabolite–metabolite (MMI), protein–protein (PPI), metabolite–protein (MPI), drug–protein (DPI), and drug–drug (DDI)—into a heterogeneous network structure. It comprises three complementary sub-models targeting direct interaction prediction, high-order association discovery, and drug-protein-metabolite triplet interaction inference. Comprehensive evaluation across 24 tissue-specific MPI networks, protein-metabolite association studies and pharmacological metabolomic datasets confirm the biological relevance, robustness and generalizability of TopoMPI for MPI prediction with AUCs ranging from 0.79 to 0.86. TopoMPI provides a scalable framework for systems-level characterization of metabolic regulation and drug mode-of-action.