Drug Combination Side Effect Prediction Based on Polypharmacy Network and GraphSAGE Algorithm
Haiqiang Xiao, Xiujuan Lei, Yuchen Zhang, Fang‐Xiang Wu · 2024
Due to the complexity and diversity of modern diseases, the combination of drugs has become the first choice. According to the graph theory in graph theory, we transform the original link prediction problem into the node identification problem by establishing the graph of drug side effect network - polypharmacy network to predict. A combined drug side effect prediction model (CDSG) was constructed on the polypharmacy network graph, based on graph attention mechanism and Graph Sample and Aggregate (GraphSAGE). Firstly, the side-effect network was constructed by using the side-effect relationship between drugs and drugs, and then the polypharmacy network was constructed. Then the target gene of the drug is encoded and the feature vector of the drug is established. Furthermore, the advanced feature representations of drugs are learned by utilizing the graph attention network and the GraphSAGE algorithm. Finally, the advanced drug characteristics were connected to the fully connected layer for classification prediction. On a baseline dataset of 1138 side effect types of 257 drugs, we conducted different methods of feature fusion experiment, ablation experiment, 5-fold crossover experiment, and compared CDSG with several computing models such as traditional GCN model, the GAT model and matrix decomposition models, and the final model achieved good results.