Knowledge Graph Information Bottleneck for Drug-Drug Interaction Prediction

Shun Liu, Gaoqi He, Kai Zhang, Honglin Li

Abstract: Drug-drug interaction (DDI) prediction is an important but challenging task in drug safety surveillance. With the accumulation of biological data, biomedical knowledge graphs (KGs) become available to model DDIs and related biological mechanisms. However, the presence of substantial noise in large-scale KGs hampers prediction performance and the identification of interpretable biological pathways. To fill the gaps, this paper proposes an information bottleneck-based (IB-based) framework that simultaneously denoises the KG and identifies key entities around drug pairs. Moreover, KG-based prediction methods rarely exploit the structural information of drug molecules. To this end, the proposed framework relates drug structures to IB objectives, together with a unique drug pair-centered readout to fuse molecular information into KG subgraph embeddings. Extensive experimental results and case studies demonstrate the effectiveness and interpretability of the framework.