Wang Maolin, Li Li, Yu Changyuan, Yan Aixia, Zhao Zhongzhen, Zhang Ge, Jiang Miao, Lu Aiping, Gasteiger Johann
State Key Laboratory of Chemical Resource Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China, State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P. O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, PR China.
Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing 100700, China, Institute of Basic Research In Clinical Medicine, China Academy of Traditional Chinese Medicine, Nanxiaojie 16#, Dongzhimennei, Beijing 100700, China. Tel.: +86 10 64014411 EXT 2397; Fax: +86 10 84032881 addresses: .
Mol Inform. 2016 Apr;35(3-4):109-15. doi: 10.1002/minf.201500115. Epub 2016 Jan 6.
Chinese Herbal Medicines (CHMs) are typically mixtures of compounds and are often categorized into cold and hot according to the theory of Chinese Medicine. This classification is essential for guiding the clinical application of CHMs. In this study, three types of molecular descriptors were used to build models for classification of 59 CHMs with typical cold/hot properties in the training set taken from the original records on properties in China Pharmacopeia as reference. The accuracy and the Matthews correlation coefficient of the models were validated by a test set containing other 56 CHMs. The best model produced the accuracies of 94.92 % and 83.93 % on training set and test set, respectively. The MACCS fingerprint model is robust in predicting hot/cold properties of the CHMs from their major constituting compounds. This work shows how a classification model for data consisting of multi-components can be developed. The derived model can be used for the application of Chinese herbal medicines.
中草药通常是化合物的混合物，并且常根据中医理论分为寒性和热性。这种分类对于指导中草药的临床应用至关重要。在本研究中，使用了三种类型的分子描述符来构建模型，以对59种具有典型寒/热特性的中草药进行分类，训练集中的这些中草药特性以《中国药典》中的原始记录为参考。通过包含其他56种中草药的测试集对模型的准确性和马修斯相关系数进行了验证。最佳模型在训练集和测试集上的准确率分别为94.92%和83.93%。MACCS指纹模型在从其主要构成化合物预测中草药的寒/热特性方面具有稳健性。这项工作展示了如何开发由多组分组成的数据的分类模型。所推导的模型可用于中草药的应用。
