基于随机森林算法的赤芍成分-靶点-心脑血管疾病网络药理作用研究

Research of compound-target-cardio-cerebral vascular disease network pharmacology of Paeoniae Radix Rubra based on random forest algorithm

目的:采用随机森林算法,构建中药赤芍成分-靶点-疾病网络,预测赤芍治疗心脑血管类疾病的关键靶标。方法:搜集KEGG数据库中小分子药物及其酶、离子通道、G蛋白、核蛋白等4类药靶数据作为训练集,建立基于随机森林法的药物-靶点相互作用模型,采用十折交叉验证评价模型精度;利用该模型对已报道的赤芍化学成分进行靶点预测,并构建其成分-靶点-疾病网络。结果:4类药靶模型的预测正确率分别为71. 36%,67. 08%,73. 71%,67. 83%;从赤芍的9个化学成分预测出多个作用靶点,预测结果得到了较好的文献验证。结论:所建模型具有较高的预测精度。

Objective:To predict key targets of Paeoniae Radix Rubra on cardio-cerebral vascular diseases and to construct the corresponding compound-target-disease network of Paeoniae Radix Rubra.Methods:Small molecule drugs and the related enzymes,ion channels,G-protein-coupled receptors,nuclear receptors downloaded from KEGG database were used as the training set.The drug-target interaction model was built by random forest algorithm,and the accuracies were evaluated by 10-fold cross-validation tests.The model was then applied to predict the potential targets interacted with the reported compounds of Paeoniae Radix Rubra and to construct a compound-target-diseases network.Results:The prediction accuracies of the four models were 71.36%,67.08%,73.71%and 67.83%,respectively.Multiple targets were predicted from the 9 chemical components of Paeoniae Radix Rubra,The predicted results were well verified by literatures.Conclusion:The models established achieve high prediction accuracies.