摘要
目的:以温胆汤为例,构建基于知识图谱和注意力机制的深度学习模型(SGRN-Trans)预测中医经典名方中药效成分与靶点的相互作用,评价其预测效果。方法:首次提出SGRN-Trans(Self-weighted Graph Relational Network-Transformer)预测模型,结合多生物数据源构建中医经典名方温胆汤知识图谱(Wendan Decoction Knowledge Graph,WDKG),利用图神经网络学习知识图谱中每个实体的低维嵌入表示,引入中药成分和靶点各自的结构特征,搭载基于注意力机制的Transformer模型进行药效成分-靶点相互作用的预测,结合分子对接及文献调研进行验证。结果:WDKG包含10个类型共14292个实体,可用于深度学习模型的研究。SGRN-Trans预测模型与TransE、TransR、ComplEx、DistMult、ConvKB等其他知识图谱嵌入模型的性能相比,效果最优。将预测排序前20组的药效成分与靶点分别进行分子对接和可视化呈现,其中8组的结合能提示其药效成分与靶点有潜在的相互作用。以温胆汤中半夏的有效成分soya-cerebroside(大豆脑苷脂)与低密度脂蛋白受体(low density lipoprotein receptor,LDLR)相互作用为例,结合研究文献进行讨论,可能是温胆汤治疗动脉粥样硬化的机制之一。结论:本研究提出基于知识图谱和注意力机制的模型SGRN-Trans,可推广用于预测中医药经典名方复杂网络体系中成分与靶点的相互作用,为阐明经典名方的药效物质基础和作用机制提供新的工具。
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.
作者
王艳菁
李治琦
魏冬青
徐威
谭红胜
Wang Yanjing;Li Zhiqi;Wei Dongqing;Xu Wei;Tan Hongsheng(Shanghai Jiao Tong University School of Life Science and Technology;Shanghai Jiao Tong University School of Medicine)
出处
《重庆医科大学学报》
CAS
CSCD
北大核心
2024年第8期1002-1011,共10页
Journal of Chongqing Medical University
基金
中央高校基本科研业务费专项资金资助-上海交通大学医工交叉研究基金重点项目(编号:YG2021ZD02)
上海交通大学“交大之星(STAR)”资助项目(编号:20230101)。
关键词
温胆汤
药物-靶点相互作用
知识图谱
图神经网络
注意力机制
Wendan Decoction
drug-target interaction
knowledge graph
graph neural network
attention mechanism
