Multi-view Feature Fusion Based on Self-attention Mechanism for Drug-drug Interaction Prediction

Abstract

Drug-drug interaction (DDI) has been a challenging problem in healthcare machine-learning research. DDI might cause changes in drug pharmacological activity and trigger adverse patient reactions. Therefore, it is critical for both patients and society to effectively identify potential DDI. Most recent studies have used graph-based learning methods to predict DDI, but these methods usually have the following limitations: i) modeling drug information on a single view; ii) ignoring the importance of different neighboring nodes; and iii) failing to integrate the drug-embedded information well. Therefore, this paper proposes a multi-view feature fusion strategy based on graph attention networks(MV-GAT). In MV-GAT, we use the graph representation learning method of bond-aware message passing neural network to obtain the local features of each atom in the molecular graph and the global features of the molecular graph. Meanwhile, We propose an attention mechanism based on a fusion strategy to handle the fusion of drug features and topological information under each view, which can efficiently integrate the features extracted from molecular and interaction maps. In addition, to ensure the diversity of node features, we use an unsupervised contrastive learning component in each Graph Neural Networks (GNN) layer to address the over-smoothing problem during information transfer. Comprehensive experiments on multiple real datasets show that MV-GAT has good generalization performance.