Predicting Drug-Drug Interactions with Graph Attention Network

Predicting Drug-Drug Interactions (DDIs) is usually a time-consuming and labour-intensive task. The undetected adverse interactions between drugs are a common cause of medical injuries. Recently, with the assistance of deep learning algorithms, the accuracy of prediction in DDIs significantly improved. However, previous methods do not take well into account both adapting to datasets and extracting neighbourhood information on the graph-structured data. In this study, we propose a new framework to fill this gap, named Interaction Prediction Graph Attention Network(IPGAT). This framework consists of two modules, i.e., the embedding module and the prediction module. Inspired by the Graph Embedding and Graph Attention Networks, the embedding module extracts features from graph-structured data with high-order neighbourhoods. Then, it directly transfers to the prediction module without an intermediate process. Our proposed IPGAT presents advantages compared to existing DDI prediction methods. Experimental results on the public DrugBank dataset reveal that IPGAT significantly outperforms the state-of-the-art methods such as AMF&AMFP, Conv-LSTM, Graph Auto-Encoder, etc. The corresponding results increase 6.9% in AUROC and at least 8.5% in AUPR for the retrospective experiment. Further studies verify the efficacy of multi-layer and multi-head in the model. The codes are available at https://github.com/yytfy/IPGAT.