In silico prediction of chemical toxicity on avian species using chemical category approaches

被引:39
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
基金
中国国家自然科学基金;
关键词
Avian toxicity; In silico prediction; Chemical category approach; Support vector machine; Information gain; QSAR MODELS; CLASSIFICATION; SUBSTRUCTURES; PARAMETERS; SELECTION; ACCURACY; INDEXES; HEALTH;
D O I
10.1016/j.chemosphere.2014.12.001
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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.
引用
收藏
页码:280 / 287
页数:8
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