Purpose – The purpose of this paper is to predict the retention times of 84 pesticides or toxicants. Design/methodology/approach – Quantitative structure – retention relationship analysis was performed on a set of 84 pesticides or toxicants using a hybrid approach genetic algorithm/multiple linear regression (GA/MLR). Findings – A model with six descriptors was developed using as independent variables. Theoretical descriptors derived from Spartan and Dragon softwares when applying GA/MLR approach. Originality/value – A six parameter linear model developed by GA/MLR, with R² of 90.54, Q² of 88.15 and S of 0.0381 in Log value. Several validation techniques, including leave-many-out cross-validation, randomization test, and validation through the test set, illustrated the reliability of the proposed model. All of the descriptors involved can be directly calculated from the molecular structure of the compounds, thus the proposed model is predictive and could be used to estimate the retention times of pesticides or toxicants.

No abstract

EU Directive for the Protection of Laboratory Animals mandates and encourages the use of alternative methods that could substitute, cut down on, and generally improve animal testing. Quantitative structure-activity relationship models (QSAR) as well as in vitro toxicity testing are among the most notable of such. QSARs are defined as computerized mathematical models that can utilize a compound’s (aromatic amine) biological activity—aquatic toxicity—to calculate or provide the experimental descriptors of the chemical structure of this compound. Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) are the approaches we use for the aim of predicting aquatic toxicity. The best models for two descriptors are the electrotopological descriptors derived from E-calc, and the partition coefficient derived by the Hyperchem software, applying a genetic algorithm—variable subset selection procedure. The important values of the statistical parameters obtained by the two approaches were as follows: By MLR: R2= 92.18, Q2 = 90.51, Q2ext= 95.26, F=188.5466, S = 0.1995. By ANN were: Q2 = 94.79, RMSE= 0.16, Q2ext= 91.71, RMSEext=0.18.

A structure / lethal dose 50 (pCIC50) relationship was researched for a set of phenols while favoring a hybrid genetic algorithm (GA) / multiple linear regression (MLR) approaches to the structural parameters being computed with (E-calc) which calcula the Kier–Hall Electrotopological state indices (E- state) and Hyperchem software. Among the more than 100 simple models with two explanatory variables acquired, we chose the model with the best values of the prediction parameter (Q2) and the coefficient of determination (R2). The reliability of the proposed model has also been illustrated using various techniques of evaluation: leave-many out, cross-validation, randomization test, and validation by the test set. pCIC50 = - 0.0835 ± (0.07006) +0.112 ± (0.007408 (logkow)2 - 0.116 ± (0.01797) s-CH3 ntot = 81 ; S= 0.3296 log unit ; Q2(%) = 74.26 ; R2 (%)= 79.24 ; F= 118.3193; P=0,000.

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.