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Chemometric modeling to predict aquatic toxicity of benzene derivatives in Pimephales Promelas
Purpose – The purpose of this paper is to predict the aquatic toxicity (LC50) of 92 substituted benzenes derivatives in Pimephales promelas. Design/methodology/approach – Quantitative structure-activity relationship analysis was performed on a series of 92 substituted benzenes derivatives using multiple linear regression (MLR), artificial neural network (ANN) and support vector machines (SVM) methods, which correlate aquatic toxicity (LC50) values of these chemicals to their structural descriptors. At first, the entire data set was split according to Kennard and Stone algorithm into a training set (74 chemicals) and a test set (18 chemical) for statistical external validation. Findings – Models with six descriptors were developed using as independent variables theoretical descriptors derived from Dragon software when applying genetic algorithm – variable subset selection procedure. Originality/value – The values of Q2 and RMSE in internal validation for MLR, SVM, and ANN model were: (0.8829; 0.225), (0.8882; 0.222); (0.8980; 0.214), respectively and also for external validation were: (0.9538; 0.141); (0.947; 0.146); (0.9564; 0.146). The statistical parameters obtained for the three approaches are very similar, which confirm that our six parameters model is stable, robust and significant.
Chemometric modeling to predict aquatic toxicity of benzene derivatives in Pimephales Promelas
Purpose – The purpose of this paper is to predict the aquatic toxicity (LC50) of 92 substituted benzenes derivatives in Pimephales promelas. Design/methodology/approach – Quantitative structure-activity relationship analysis was performed on a series of 92 substituted benzenes derivatives using multiple linear regression (MLR), artificial neural network (ANN) and support vector machines (SVM) methods, which correlate aquatic toxicity (LC50) values of these chemicals to their structural descriptors. At first, the entire data set was split according to Kennard and Stone algorithm into a training set (74 chemicals) and a test set (18 chemical) for statistical external validation. Findings – Models with six descriptors were developed using as independent variables theoretical descriptors derived from Dragon software when applying genetic algorithm – variable subset selection procedure. Originality/value – The values of Q2 and RMSE in internal validation for MLR, SVM, and ANN model were: (0.8829; 0.225), (0.8882; 0.222); (0.8980; 0.214), respectively and also for external validation were: (0.9538; 0.141); (0.947; 0.146); (0.9564; 0.146). The statistical parameters obtained for the three approaches are very similar, which confirm that our six parameters model is stable, robust and significant.
Chemometric modeling to predict aquatic toxicity of benzene derivatives in Pimephales Promelas
Ziani, Nadia (author) / Amirat, Khadidja / Messadi, Djelloul
2016
Article (Journal)
English
Comparative toxicity of chlorothalonil and chlorpyrifos: Ceriodaphnia dubia and Pimephales promelas
| Online Contents | 2002