SRR-DDI: A drug-drug interaction prediction model with substructure refined representation learning based on self-attention mechanism

Drug-drug interaction (DDI) is an important safety issue during clinical treatment, where the mechanism of action of drugs may interfere with each other thereby causing adverse effects on the body leading to therapeutic failure. Since the deep learning method provide a powerful tool in DDI prediction, in this paper, we propose a DDI prediction model based on substructure Refined Representation Learning based on Self -Attention Mechanism, SRR-DDI, to improve the robustness of the substructure features determining the properties of the drug, thus improving the performance of DDI prediction. To improve the generalization of the model, the drug similarity feature is designed and introduced to help the model extract potential associations between drugs. The comparative experiments are set up on real -word data with two scenarios of warm start and cold start for performance evaluation, and the experimental results show that SRR-DDI outperforms the state-ofthe-art methods. Finally, the visual interpretation experiment demonstrates that SRR-DDI can gradually refine the substructure features, and highlight the important substructures of drugs in DDI. In summary, SRR-DDI provides a powerful tool for predicting DDI and in-depth understanding of DDI.