Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction
Honglei Bai, Siyuan Lu, Tiangang Zhang, Hui Cui, Toshiya Nakaguchi, Ping Xuan · iScience · 2024
Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches. • Two heterogeneous graphs were constructed based on two drug similarities • We established the relational transformer module to enhance the semantic information • Two knowledge distillation strategies were designed to extract knowledge • Adaptive convolutional neural networks were constructed to distinguish features Drugs; Artificial intelligence