Motif-Oriented Representation Learning with Topology Refinement for Drug-Drug Interaction Prediction

Abstract

Drug-Drug Interaction (DDI) prediction has attracted considerable attention in designing multi-drug combination strategies and avoiding adverse reactions. Notably, Artificial Intelligence (AI)-driven DDI prediction methods have emerged as a pivotal research paradigm. However, most AI-driven DDI prediction methods fall short in exploring intra-molecular motifs, and heavily rely on the overly idealized assumption of the complete inter-molecular topology, limiting their expressive capacities. To this end, we propose a Motif-Oriented representation learning with TOpology Refinement for DDI prediction, namely MOTOR, to exploit both the multi-granularity motif information and the topological structure of DDI networks. Specifically, MOTOR effectively captures motif internal structures, motif local contexts, and motif global semantics. Furthermore, MOTOR employs an iterative learning strategy to continuously refine the DDI topology and optimize the corresponding drug representations. Extensive experimental results demonstrate that MOTOR exhibits superior performance with interpretable insights in DDI prediction tasks across three real-world datasets, thereby opening up new avenues in AI-driven DDI prediction.