DualC: Drug-Drug Interaction Prediction Based on Dual Latent Feature Extractions

Abstract

Drug-Drug Interaction is characterized by a modification in the action of one drug due to its concurrent use with another. It involves the safety and universality of drugs, and is one of the most meaningful issues in clinical drug combination therapy and drug development. We prefer to use computational methods to achieve DDI prediction in order to achieve large-scale prediction. This article designs a DDI prediction model DualC based on the layer attention mechanism of Graph Convolutional Network and 1 Dimensional-Convolutional Neural Network to extract topological and structural information of drugs. First, the DDI network is obtained from the drug relationship data in the database and the drug similarity network is calculated with the help of drug target features, then they are constructed into a heterogeneous network. Next, the layer attention mechanism and Graph Convolutional Network are used to learn the topological information. Subsequently, the structural information is acquired from the chemical substructure similarity matrix utilizing 1 Dimensional-Convolutional Neural Network. Finally, use the sigmoid function for DDI prediction. The experimental results show advantages of DualC which AUC reaches 0.965 and ACC reaches 0.973. The case study further proves DualC has certain practical significance.