Prediction of component-target interactions in Wendan Decoction based on the artificial intelligence SGRN-Trans framework

Wang Yanjing, Li Zhiqi, Wei Dongqing, Xu Wei, Tan Hongsheng

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Journal of Chongqing Medical University ›› 2024, Vol. 49 ›› Issue (08) : 1002-1011. DOI: 10.13406/j.cnki.cyxb.003555
Bioinformatics

Prediction of component-target interactions in Wendan Decoction based on the artificial intelligence SGRN-Trans framework

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Abstract

Objective To construct a deep learning model（SGRN-Trans） based on knowledge graph and attention mechanism for predicting 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 proposed 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 review 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 components and targets，among which 8 combinations suggested the potential interaction between pharmacodynamic components and targets. With the interaction between soya-cerebroside （an effective constituent 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 components and targets in the complex network system of classic TCM prescriptions，which provides a new tool for clarifying the pharmacodynamic material basis of classic TCM prescriptions and related mechanisms of action.

Key words

Wendan Decoction / drug-target interaction / knowledge graph / graph neural network / attention mechanism

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Wang Yanjing , Li Zhiqi , Wei Dongqing , et al . Prediction of component-target interactions in Wendan Decoction based on the artificial intelligence SGRN-Trans framework. Journal of Chongqing Medical University. 2024, 49(08): 1002-1011 https://doi.org/10.13406/j.cnki.cyxb.003555

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