YE Li1, WANG Xin-zhou1, ZHU Yong-liang1*, JIN Ruo-min2*, YE Zu-guang3, YAO Guang-tao2, LIU Jing-ge1, QIAN Xiang-ping1
1.Suzhou NeuPharma Co., Ltd., Suzhou 215123, China; 2. Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; 3. China Academy of Chinese Medical Sciences, Beijing 100700, China
Abstract��
OBJECTIVE To build tree models for the prediction of hepatotoxicity of compounds from traditional Chinese medicines (TCM).METHODS Three hundred and forty-eight compounds (256 with hepatotoxicity and 92 without) were collected from various databases and literatures and used as training set to build tree models. Twenty-two compounds identified from TCM were first tested for hepatotoxicity experimentally and then used as test set to evaluate the prediction accuracy of optimal tree models. RESULTS Models built with random forest algorithm had the highest overall predictive accuracy of 85% (leave-one-out), but had much lower accuracy for hepatotoxicity negative compounds compared to hepatotoxicity positive compounds (more positive bias). The model built with boosted decision tree had a similar overall predictive accuracy and a much less bias, and therefore was selected as the optimal model. The prediction accuracy of the 22 test samples was 73% by the optimal model. The optimal model based on the training set containing both synthetic and TCM compounds had less bias than an optimal model based on a training set containing only the synthetic compounds. CONCLUSION Tree models with high predictive accuracy are built based on a training set consisting of both synthetic and TCM compounds.The optimal models can predict the hepatotoxicity of TCM compounds with reasonable accuracy.
YE Li-,
WANG Xin-Zhou-,
ZHU Yong-Liang-* etc
.Predicting Hepatotoxicity of Compounds from Traditional Chinese Medicines Using Tree Models[J] Chinese Pharmaceutical Journal, 2014,V49(18): 1583-1588
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2014, Vol. 49 
