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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Classification of drugs according to their milk/plasma concentration ratio.
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Classification of drugs according to their milk/plasma concentration ratio.

机译：根据其乳/血浆浓度比对药物进行分类。

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摘要

The classification of drugs was done according to their milk/plasma concentration ratio (M/P) by using counter propagation artificial neural network (CP-ANN). The features of each drug were encoded by linear free energy relationship (LFER) parameters. These descriptors were used as inputs for developing linear discriminant analysis, quadratic discriminant analysis, least square support vector machine and CP-ANN models to distinguish the potential risk of 154 drugs as high risk (with M/P > 1) and low risk (with M/P < 1) for lactating women. The accuracy of classification for training, internal and external test sets was 100.00%, 100.00% and 90.00%, respectively for CP-ANN model, as the best model. The obtained results revealed the applicability of CP-ANN in classification of drugs based on their M/P values, using LFER parameters.
机译：通过使用计数器传播人工神经网络（CP-ANN），根据其牛奶/血浆浓度比（M / P）进行药物的分类。 每种药物的特征通过线性自由能量关系（LEFE）参数编码。 这些描述符被用作开发线性判别分析的输入，二次判别分析，最小二乘支持向量机和CP-ANN模型，将154种药物的潜在风险区分为高风险（具有M / P> 1）和低风险（与 m / p <1）用于哺乳期妇女。 对于CP-Ann Model，培训，内部和外部测试集分类，内部和外部测试集的准确性分别为100.00％，100.00％和90.00％，作为最佳型号。 所获得的结果揭示了CP-ANN在使用LET参数的基于M / P值的药物分类中的适用性。

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