PHGL-DDI: A pre-training based hierarchical graph learning framework for drug-drug interaction prediction

Yongna Yuan, Jiaqi Yue, Ruisheng Zhang, Wei Su

Abstract: In the treatment of complex diseases, patients often need to use multiple types of drugs at the same time. However, drug-drug interactions (DDIs) often lead to side effects and aggravate disease symptoms. Therefore, accurate prediction of drug interactions is essential to reduce medical risks. Currently, artificial intelligence methods have received increasing attention in exploring potential DDIs. However, the existing techniques usually only extract the features inside the drug molecule or in the DDI network, which is difficult to obtain the necessary information comprehensively. To solve this problem, a drug-drug interaction hierarchical graph neural network model called PHGL-DDI is proposed, which considers both the graph-level information of drugs and the network-level information of DDI. Specifically, each node in the DDI network view represents a drug, and the connections between nodes represent the interactions between drugs. Meanwhile, in the drug graph-level, the self-supervised contrast learning method is employed for pre-training the graph neural network, so as to extract the features inside drugs, thereby building a powerful DDI understanding system. Compared with the link prediction method that only extracts DDI network features, PHGL-DDI not only uses the topological structure information between nodes, but also considers the semantic features of entity nodes. Extensive experimental studies show that PHGL-DDI significantly improves the prediction effect of DDI compared with the most advanced methods, and has better generalization ability.