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Prediction of placenta barrier permeability and reproductive toxicity of compounds in tocolytic Chinese herbs using support vector machine

Fang LuYusu HeLudi JiangJiahua ChenYilian CaiYanling Zhang

Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Material Medica, Beijing University of Chinese Medicine

摘要:93 compounds which can permeate the placenta barrier were collected as data set for the construction of support vector regression(SVR) model. Besides, 140 compounds with reproductive toxicity and 170 compounds with no reproductive toxicity were collected as another data set for the construction of support vector classification(SVC) model. 1481 molecular descriptors were calculated to represent the structure characteristics of all the compounds mentioned above by Dragon2.1. Cfs Subset Eval valuation method and Best First-D1-N5 searching method were used to optimize the subset of molecular descriptors. Then based on the above data, SVR model for prediction the placenta barrier permeability(PBP) and SVC model for prediction the reproductive toxicity were built respectively by using Lib SVM program. Both the SVR model and the SVC model obtained better prediction ability. The correlation coefficient(R2) values of the training set and test set of the optimal SVR model were 0.990 and 0.780. The accuracy, sensitivity, and specificity values of the optimal SVC model were all above 80%. Subsequently, the SVR model was utilized to predict the PBP of the compounds which were collected from 13 commonly used tocolytic Chinese herbs. The compounds with higher permeability were further studied by the SVC model and 15 compounds were classified as positive compounds with reproductive toxicity. The two models constructed in this study might be employed in guiding the application of the tocolytic Chinese herbs in clinical.
会议名称:

2015 International Conference on Materials Engineering and Information Technology Applications(MEITA 2015)

会议时间:

2015-08-30

会议地点:

中国广西桂林

  • 专辑:

    医药卫生科技

  • 专题:

    中药学

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    R285.1

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