Drug side effects prediction via cross attention learning and feature aggregation

The issue of drug safety has received increasing attention in modern society. Estimating the frequency of drug side effects proves to be an effective approach to improving drug development safety. Clinical trials are the most widely used manner in the medical field, but their long duration and high labor costs are always challenging for researchers. Many drug side effect frequency prediction methods based on graph neural networks have also been proposed and achieved good results. However, most current mainstream methods extract feature information separately for drugs and side effects but fail to capture their interaction information, which seriously degenerates the final prediction performance. To solve this problem, we have proposed a network to explore the underlying relationship between drugs and the frequency of side effects by combining graph attention learning, cross attention interaction, and feature aggregation into a unified framework. In this network, we first use the graph attention mechanism to accomplish effective feature extraction for drugs and side effects, respectively. Then, by designing a cross-attention interaction module, the chemical characteristics of each atom of the drug and the correlation between the side effects are captured to gather information on the interaction between the drug and the side effects. Subsequently, we fully fuse graph attention features and interaction attention features by embedding a feature fusion module to obtain enhanced features that fuse drugs and side effects. Finally, the predicted frequency is obtained using matrix inner product operation. Experimental results on the SIDER dataset show that our proposed method achieves the best performance when compared to previous state-of-the-art methods in both warm start and cold start scenarios. We also conducted ablation experiments to demonstrate the effectiveness of different modules embedded in the network. The code, datasets, and materials are available at https://github.com/zixiaojin66/A-3Net-master. In addition, we construct a user-friendly web server for testing at: https://a3net666.streamlit.app/.