文献类型：期刊文献

英文题名：In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods

作者：Cheng, Feixiong[1]; Shen, Jie[1]; Yu, Yue[1]; Li, Weihua[1]; Liu, Guixia[1]; Lee, Philip W.[1,2]; Tang, Yun[1]

机构：[1] Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China; [2] Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan

年份：2011

卷号：82

期号：11

起止页码：1636

外文期刊名：Chemosphere

收录：EI(收录号：20110813685623)

语种：英文

外文关键词：Support vector machines - Accident prevention - Industrial chemicals - Pattern recognition - Radial basis function networks - Organic chemicals - Decision trees - Nearest neighbor search

摘要：There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. ? 2010 Elsevier Ltd.