目的 基于网络药理学方法探讨、筛选经典名方逍遥散治疗糖尿病的可能通路、靶点、成分。方法 利用TCMSP、TCMIP、Drugbank、TTD等数据库检索成分、靶点信息，并进行Cytoscape分析；利用String数据库进行蛋白互作分析，利用ClueGO进行GO等数据库信息富集，并以Autodock Vina进行分子对接验证。结果 检索得到逍遥散394个可能的活性成分和558个成分靶点，糖尿病相关靶点4 963个，靶点取交集后得到359个靶点，在Cytoscape进行分析并按Degree排序后取前10个靶点。按Degree、Average Shortest Path Length、Betweenness Centrality和Closeness Centrality排序后各取排名靠前的一半数据得到325个靶点，在String数据库中进行检索利用Cytoscape分析，取Degree大于100的靶点，与中药-成分-靶点网络分析中的10个靶点去重合并后得到21个靶点，对这21个靶点进行分子对接验证和GO、KEGG、REACTOME数据库检索分析。结论 筛选出8个潜在活性结构，利用分子对接在21个潜在关键靶点中筛选出1个与成分结合非常强的靶点即NOS2。

Objective To investigate and screen related pathways, targets and ingredients of classical formula Xiaoyao Powder in treating diabetes based on network pharmacology. Methods The databank TCMSP and TCMSP were used to search and retrieve information of ingredients and targets of Xiaoyao Powder, then data was processed with Cytoscape, protein relationship was analysed by String, the information of GO was enriched by ClueGO, the results were validated by Autodock Vina. Results From TCMSP and TCMIP, 394 ingredients and 558 targets of Xiaoyao Powder were obtained. From GenCLiP3, DisGeNET, DrugBank, OMIM, TTD got 4 963 targets related to diabetes were obtained. Herb-ingredient-target network was created and analyzed by Cytoscape, the results were arranged by Degree, the first ten targets was take to complete a further discussion. The results by Degree, Average Shortest Path Length, Betweenness Centrality and Closeness Centrality were arranged separately, half of each part got 325 targets in total, in String databank, the results were arranged by Degree, the target which has a higher degree value than 100 were analysed, together with the 10 targets above, and with the help of ClueGO the information of Pathway, Molecular Function, etc were enriched. Conclusion Eight molecules may be active to diabetes targets. Among 21 targets, one target, NOS2, can combine with those ingredients in Xiaoyao Powder through docking results.

R285.5

天津市医药科学研究所科研团队项目（STD-1）