Drug-drug Interaction Prediction with Common Structural Patterns

Substructures of drugs are important for drug-drug interaction (DDI) prediction because drugs with similar chemical structures are prone to share similar properties. There are common substructures (i.e., functional groups) that play significant roles in DDI prediction. However, the existing computational methods can't fully utilize common structural patterns between drugs for DDI prediction. In this paper, we develop a substructure-based framework named StructDDI which can fully utilize common structural patterns between drugs. A graph processing method based on the random walk is proposed to generate the representation of drugs. A novel feature extraction component that includes dual convolutional neural networks (CNNs) is proposed to automatically summarize structural and chemical representation. The proposed StructDDI was evaluated on two real-world datasets and performed better than state-of-the-art baselines.