文献类型：期刊文献

英文题名：In silico prediction of chemical toxicity on avian species using chemical category approaches

作者：Zhang, Chen[1];Cheng, Feixiong[1];Sun, Lu[1];Zhuang, Shulin[2];Li, Weihua[1];Liu, Guixia[1];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]Zhejiang Univ, Inst Environm Sci, Coll Environm & Resource Sci, Hangzhou 310058, Zhejiang, Peoples R China

年份：2015

卷号：122

起止页码：280

外文期刊名：CHEMOSPHERE

收录：;EI(收录号：20145200369520);WOS:【SCI-EXPANDED(收录号:WOS:000348952300038)】；

基金：We sincerely thank Dr. Chihae Yang of Altamira LLC and Dr. James Rathman of Ohio State University for their helpful advice on this study. The work was supported by the National Natural Science Foundation of China (Grants 81373329 and 21107094), the 863 Project (Grant 2012AA020308), the Fundamental Research Funds for the Central Universities (Grant WY1113007), and the 111 Project (Grant B07023).

语种：英文

外文关键词：Avian toxicity; In silico prediction; Chemical category approach; Support vector machine; Information gain

摘要：Avian species are sensitive to pesticides and industrial chemicals, and hence used as model species in evaluation of chemical toxicity. In present study, we assessed the toxicity of more than 663 diverse chemicals on 17 avian species. All the chemicals were classified into three categories, i.e. highly toxic, slightly toxic and non-toxic, based on the toxicity classification criteria of the United States Environmental Protection Agency (EPA). To evaluate these chemicals, the toxicity prediction models were built using chemical category approaches with molecular descriptors and five commonly used fingerprints, in which five machine learning methods were performed on two standard test species: aquatic bird mallard duck and terrestrial bird northern bobwhite quail. The support vector machine (SVM) method with Pubchem fingerprint performed best as revealed by 5-fold cross-validation and the external validation set on Japanese quail. No species difference existed in our database despite several chemicals with different toxicity on some avian species. The best model had an overall accuracy at 0.851 for the prediction of toxicity on avian species, which outperformed the work of Mazzatorta et al. Furthermore, several representative substructures for characterizing avian toxicity were identified via information gain (IG) method. This study would provide a new tool for chemical safety assessment. (C) 2014 Elsevier Ltd. All rights reserved.