DSE-HNGCN: Predicting the frequencies of drug-side effects based on heterogeneous networks with mining interactions between drugs and side effects.

Evaluating the frequencies of drug-side effects is crucial in drug development and risk-benefit analysis. While existing deep learning methods show promise, they have yet to explore using heterogeneous networks to simultaneously model the various relationship between drugs and side effects, highlighting areas for potential enhancement. In this study, we propose DSE-HNGCN, a novel method that leverages heterogeneous networks to simultaneously model the various relationships between drugs and side effects. By employing multi-layer graph convolutional networks, we aim to mine the interactions between drugs and side effects to predict the frequencies of drug-side effects. To address the over-smoothing problem in graph convolutional networks and capture diverse semantic information from different layers, we introduce a layer importance combination strategy. Additionally, we have developed an integrated prediction module that effectively utilizes drug and side effect features from different networks. Our experimental results, using benchmark datasets in a range of scenarios, show that our model outperforms existing methods in predicting the frequencies of drug-side effects. Comparative experiments and visual analysis highlight the substantial benefits of incorporating heterogeneous networks and other pertinent modules, thus improving the accuracy of DSE-HNGCN predictions. We also provide interpretability for DSE-HNGCN, indicating that the extracted features are potentially biologically significant. Case studies validate our model's capability to identify potential side effects of drugs, offering valuable insights for subsequent biological validation experiments.