title: In silico prediction of chemical toxicity on avian species using chemical category approaches.
abstract: 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.
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Feixiong
authors.initials: F
authors.lastname: Cheng
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Lu
authors.initials: L
authors.lastname: Sun
authors.affiliation: Institute of Environmental Sciences, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
authors.forename: Shulin
authors.initials: S
authors.lastname: Zhuang
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Li
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. Electronic address: philiplee2007@gmail.com.
authors.forename: Philip W
authors.initials: PW
authors.lastname: Lee
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. Electronic address: ytang234@ecust.edu.cn.
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
mesh_terms: D000818:Animals; D001717:Birds; D062067:Chemical Safety; D003198:Computer Simulation; D054750:Ecotoxicology; D004785:Environmental Pollutants / Q000633:toxicity*; D007928:Lethal Dose 50; D008962:Models, Theoretical; D060388:Support Vector Machine; D014481:United States; D014484:United States Environmental Protection Agency
chemical_list: D004785:Environmental Pollutants
keywords: Avian toxicity; Chemical category approach; In silico prediction; Information gain; Support vector machine