文献类型：期刊文献

英文题名：In silico Prediction of Chemical Ames Mutagenicity

作者：Xu, Congying[1];Cheng, Feixiong[1];Chen, Lei[1];Du, Zheng[1];Li, Weihua[1];Liu, Guixia[1,2];Lee, Philip W.[1];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Shanghai Key Lab New Drug Design, Sch Pharm, Shanghai 200237, Peoples R China;[2]Chinese Acad Sci, State Key Lab Drug Res, Shanghai Inst Mat Med, Shanghai 201203, Peoples R China

年份：2012

卷号：52

期号：11

起止页码：2840

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20124815741064);WOS:【SCI-EXPANDED(收录号:WOS:000311461400005)】；

基金：We gratefully acknowledged the financial supports from the 863 Project (Grant 2012AA020308), the Fundamental Research Funds for the Central Universities (WY1113007), the State Key Laboratory of Drug Research (Grant SIMM1203KF-13), and the Shanghai Committee of Science and Technology (Grant 11DZ2260600).

语种：英文

外文关键词：Decision trees - Forecasting - Neural networks - Nearest neighbor search

摘要：Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness. in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the basis. of this data set, high predictive models were then built using five machine learning methods, namely support Vector machine (SVM), C4.5 decision free (C4.5 DT), artificial neural network (ANN), k-nearest neighbors (kNN), and naive Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys.(MACCS) PubChem fingerprint (PubChem) and Substructure fingerprint (SubFP). Performances were measured by cross Validation and an external test set containing 831 diverse chemicals. Information.:Information gain and substructure analysis were used to interpret the models. The accuracies of fivefold Cross Validation were from 0.808 to 0.841 for top five Models. The, range of accuracy:for the,external validation set was from 0.904 to 0.980, which outperformed that of Toxtree., Three models (PubChem-kNN, MACCS-kNN, and PubChem-SVM) showed high and reliable,predictive accuracy. for the mutagens and nonmutagens and, hence, could he used in prediction of chemical Ames Mutagenicity: