title: In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.
abstract: 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.
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Feixiong
authors.initials: F
authors.lastname: Cheng
authors.forename: Jie
authors.initials: J
authors.lastname: Shen
authors.forename: Yue
authors.initials: Y
authors.lastname: Yu
authors.forename: Weihua
authors.initials: W
authors.lastname: Li
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.forename: Philip W
authors.initials: PW
authors.lastname: Lee
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003663:Decision Trees; D015386:Hazardous Substances / Q000145:classification / Q000633:toxicity*; D007221:Industry; D016015:Logistic Models; D021281:Quantitative Structure-Activity Relationship; D018570:Risk Assessment / Q000379:methods; D013769:Tetrahymena pyriformis / Q000187:drug effects*; D018675:Toxicity Tests / Q000379:methods*
chemical_list: D015386:Hazardous Substances