MRGCDDI: Multi-Relation Graph Contrastive Learning Without Data Augmentation for Drug-Drug Interaction Events Prediction

Predicting drug-drug interactions (DDIs) is a significant concern in the field of deep learning. It can effectively reduce potential adverse consequences and improve therapeutic safety. Graph neural network (GNN)-based models have made satisfactory progress in DDI event prediction. However, most existing models overlook crucial drug structure and interaction information, which is necessary for accurate DDI event prediction. To tackle this issue, we introduce a new method called MRGCDDI. This approach employs contrastive learning, but unlike conventional methods, it does not require data augmentation, thereby avoiding additional noise. MRGCDDI maintains the semantics of the graphical data during encoder perturbation through a simple yet effective contrastive learning approach, without the need for manual trial and error, tedious searching, or expensive domain knowledge to select enhancements. The approach presented in this study effectively integrates drug features extracted from drug molecular graphs and information from multi-relational drug-drug interaction (DDI) networks. Extensive experimental results demonstrate that MRGCDDI outperforms state-of-the-art methods on both datasets. Specifically, on Deng's dataset, MRGCDDI achieves an average increase of 4.33% in accuracy, 11.57% in Macro-F1, 10.97% in Macro-Recall, and 10.64% in Macro-Precision. Similarly, on Ryu's dataset, the model shows improvements with an average increase of 2.42% in accuracy, 3.86% in Macro-F1, 3.49% in Macro-Recall, and 2.75% in Macro-Precision.