title: Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches.
abstract: In this study, we established nonlinear quantitative-structure toxicity relationship (QSTR) models for predicting the toxicities of chemical pesticides in multiple aquatic test species following the OECD (Organization for Economic Cooperation and Development) guidelines. The decision tree forest (DTF) and decision tree boost (DTB) based QSTR models were constructed using a pesticides toxicity dataset in Selenastrum capricornutum and a set of six descriptors. Other six toxicity data sets were used for external validation of the constructed QSTRs. Global QSTR models were also constructed using the combined dataset of all the seven species. The diversity in chemical structures and nonlinearity in the data were evaluated. Model validation was performed deriving several statistical coefficients for the test data and the prediction and generalization abilities of the QSTRs were evaluated. Both the QSTR models identified WPSA1 (weighted charged partial positive surface area) as the most influential descriptor. The DTF and DTB QSTRs performed relatively better than the single decision tree (SDT) and support vector machines (SVM) models used as a benchmark here and yielded R(2) of 0.886 and 0.964 between the measured and predicted toxicity values in the complete dataset (S. capricornutum). The QSTR models applied to six other aquatic species toxicity data yielded R(2) of >0.92 (DTF) and >0.97 (DTB), respectively. The prediction accuracies of the global models were comparable with those of the S. capricornutum models. The results suggest for the appropriateness of the developed QSTR models to reliably predict the aquatic toxicity of chemicals and can be used for regulatory purpose.
authors.affiliation: Kan Ban Systems Pvt. Ltd., Laxmi Nagar, Delhi 110092, India. Electronic address: nikitabasant_83@yahoo.com.
authors.forename: Nikita
authors.initials: N
authors.lastname: Basant
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India. Electronic address: gpshikha@gmail.com.
authors.forename: Shikha
authors.initials: S
authors.lastname: Gupta
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India. Electronic address: kpsingh_52@yahoo.com.
authors.forename: Kunwar P
authors.initials: KP
authors.lastname: Singh
mesh_terms: D059001:Aquatic Organisms / Q000187:drug effects*; D003663:Decision Trees; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D010575:Pesticides / Q000737:chemistry / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D013045:Species Specificity; D014874:Water Pollutants, Chemical / Q000737:chemistry / Q000633:toxicity*
chemical_list: D010575:Pesticides; D014874:Water Pollutants, Chemical
keywords: Algae; Aquatic toxicity; Chemical pesticides; Global QSTRs; Multi-species QSTRs; Structural diversity