ABSTRACT
Drug-drug interaction (DDI) indicates where a particular drug's desired course of action is modified when taken with other drug (s). DDIs may hamper, enhance, or reduce the expected effect of either drug or, in the worst possible scenario, cause an adverse side effect. While it is crucial to identify drug-drug interactions, it is quite impossible to detect all possible DDIs for a new drug during the clinical trial. Therefore, many computational methods are proposed for this task. This paper presents a novel method based on a heterogeneous information network (HIN), which consists of drugs and other biomedical entities like proteins, pathways, and side effects. Afterward, we extract the rich semantic relationships among these entities using different meta-path-based topological features and facilitate DDI prediction. In addition, we present a heterogeneous graph attention network-based end-to-end model for DDI prediction in the heterogeneous graph. Experimental results show that our proposed method accurately predicts DDIs and outperforms the baselines significantly.
