Abstract
Drug-Drug Interactions (DDIs) can occur when diseases are treated with combinations of drugs, leading to changes in the pharmacological activity of these drugs. Predicting DDIs has become a crucial task in medical health. Recently, hierarchical graph representation learning methods have attracted significant interest and have proven effective for this task. However, collecting drug interaction data through biological experiments in wet laboratories is resource- and time-intensive. Given the limited amount of available drug interaction data, the performance of existing hierarchical graph methods has encountered a bottleneck. Current approaches are supervised learning methods, which train graph neural networks on specific datasets and can cause overfitting problems. Additionally, supervised learning models cannot leverage information from massive amounts of unlabeled public molecular datasets, such as ZINC15. To overcome this limitation, we propose a novel method for multi-view graph representation learning, namely, Self-Supervised Multi-View Graph Representation Learning for Drug-Drug Interaction Prediction (SMG-DDI). SMG-DDI leverages a pre-trained Graph Convolutional Network to generate inter-view molecule graph representations, incorporating atoms as nodes and chemical bonds as edges. Subsequently, SMG-DDI captures intra-view interactions between molecules. The final drug-drug interactions will be based on the drug embeddings from intra-view analyses. Our experiments conducted on various real datasets demonstrate that molecular structure information can aid in predicting potential drug-drug interactions, and our proposed approach outperforms state-of-the-art DDI prediction methods. The accuracies are 0.83, 0.79, and 0.73 on small, medium, and large scale test datasets, respectively.