基于网络药理学方法预测银杏叶治疗心脑血管类疾病的有效成分及其潜在靶点

目的:采用网络药理学方法,研究银杏叶"成分-靶点-心脑血管相关疾病"复杂网络,预测银杏叶治疗心脑血管类疾病的有效成分及其潜在靶点。方法:建立药物分子-靶点相互作用的随机森林预测模型,对银杏叶化学成分进行靶点预测,然后构建和分析银杏叶"成分-靶点-心脑血管相关疾病"复杂网络。结果:对银杏叶的34个化学成分预测出多个作用靶点,结果得到了文献验证;进一步网络分析的结果初步揭示了银杏叶治疗心脑血管类疾病的有效成分和潜在靶点。结论:本方法可用于研究中药的物质基础和分子作用机制。

Predicting Effective Components and Potential Targets of Ginkgo biloba on Cardio-cerebral Vascular Diseases Based on Network Pharmacology

Objective:To construct the complicated compound-target-cardio-cerebral-vascular-disease network of Ginkgo biloba and to predict effective components and potential targets of G. biloba on cardio-cerebral vascular diseases by network pharmacology. Method:The drug-target interaction model was established by random forest algorithm, which was then applied to predict the potential targets interacted with compounds of G. biloba. The complicated compound-target-disease network of G. biloba was then constructed and analyzed. Result:The predicted targets of 34 compounds of G. biloba were validated by literatures. Furthermore, network analysis was performed, preliminarily revealing the active ingredients and potential targets of G. biloba in treatment of cardio-cerebral vascular diseases. Conclusion:The present approach can be used to study the material base and the molecular mechanism of traditional Chinese medicine.