文献类型：期刊文献

英文题名：In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts

作者：Sun, Lu[1];Zhang, Chen[1];Chen, Yingjie[1];Li, Xiao[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, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China;[2]Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Zhejiang, Peoples R China

年份：2015

卷号：4

期号：2

起止页码：452

外文期刊名：TOXICOLOGY RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000350552900026)】；

基金：The authors would like to thank Dr Chihae Yang of Altamira LLC for providing the software ChemoTyper and for the helpful comments on the manuscript. This study was supported by the National Natural Science Foundation of China (grant 81373329), the 863 Project (grant 2012AA020308) and the Fundamental Research Funds for the Central Universities (grant WY1113007).

语种：英文

摘要：Aquatic toxicity is an important endpoint in the evaluation of chemically adverse effects on ecosystems. In this study, in silico models were developed for the prediction of chemical aquatic toxicity in different fish species. Firstly, a large data set containing 6422 data points on aquatic toxicity with 1906 diverse chemicals was constructed. Using molecular descriptors and fingerprints to represent the molecules, local and global models were then developed with five machine learning methods based on three fish species (rainbow trout, fathead minnow and bluegill sunfish). For the local models, both binary and ternary classification models were obtained for each of the three fish species. For the global models, data of all the three fish species were used together. The predictive accuracy of both the local and global models was around 0.8 for the test sets. Moreover, data of the sheepshead minnow were used as an external validation set. For the best local model (model 2), the predictive accuracy was 0.875 for the sheepshead minnow, while for the best global model (model 14), the predictive accuracy was 0.872 for the sheepshead minnow. The FN compounds in model 2 and model 14 were 18 and 10, respectively. Hence, model 14 was the best model, and thus could predict the toxicity of other fish species'. Furthermore, information gain and ChemoTyper methods were used to identify toxic substructures, which could significantly correlate with chemical aquatic toxicity. This study provides critical tools for an early evaluation of chemical aquatic toxicity in an environmental hazard assessment.