Objective:To construct a deep learning model(SGRN-Trans)based on knowledge graph and attention mechanism for pre-dicting the interaction between pharmacodynamic components and targets in classic traditional Chinese medicine(TCM)prescriptions with Wendan Decoction as an example,and to assess its predictive performance.Methods:The SGRN-Trans predictive model was pro-posed for the first time.Multiple biological data sources were used to construct the knowledge graph of Wendan Decoction(WDKG),and graph neural networks were used to learn the low-dimensional embedding representation of each entity in the knowledge graph.The respective structural features of TCM components and targets were introduced,and the Transformer model based on attention mechanism was used to predict the interaction between pharmacodynamic components and targets.Molecular docking and literature re-view were used for validation.Results:WDKG contained 10 types of entities,with 14292 entities in total,which could be used for the research on deep learning models.The SGRN-Trans predictive model showed the best performance compared with other knowledge graph embedding models such as TransE,TransR,ComplEx,DistMult,and ConvKB.Molecular docking and visualized presentation were performed for the top 20 groups of pharmacodynamic compo-nents and targets,among which 8 combinations suggested the poten-tial interaction between pharmacodynamic components and targets.With the interaction between soya-cerebroside(an effective con-stituent of Pinellia ternata in Wendan Decoction)and low-density lipoprotein receptor as an example,the literature review showed that it might be one of the mechanisms for Wendan Decoction in the treatment of atherosclerosis.Conclusion:The SGRN-Trans model based on knowledge graph and attention mechanism proposed in this study can be widely used to predict the interaction between com-ponents and targets in the complex network system of classic TCM prescriptions,which provides a new tool for clarifying the pharmaco-dynamic material basis of classic TCM prescriptions and related mechanisms of action.
维普
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