MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug-Drug Interactions

Drug-drug interactions (DDIs) can severely affect patient health and safety. Predicting potential DDIs before administering medication to patients is crucial for drug development as it helps to prevent adverse drug reactions. Many effective DDI prediction methods have been proposed using graph neural networks; however, these methods only aggregate information from directly connected nodes restricted to a drug-related manner and fail to capture long-range dependencies in heterogeneous networks. To address this issue, we propose an enhanced multiple-feature hybrid graph method to predict DDIs (MFHG-DDI). Specifically, we constructed a heterogeneous network incorporating multiple drug features and DDI information. We employed an enhanced hybrid graph module that integrates a graph convolutional network, graph attention network, and global average pooling to learn latent features, ultimately applying a prediction function to predict DDIs. MFHG-DDI reframes the heterogeneous network as a graph classification task, capturing DDI information efficiently through an enhanced hybrid graph. Known DDI datasets were used to train and evaluate the proposed model. The experimental results indicate that integrating multiple drug features into the hybrid graph method can enhance the DDI prediction accuracy, increases the success rate of combination therapy, and has the potential to enhance drug safety.