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The purpose of this paper is to predict the octanol/water partition coefficient (K_ow) of 43 organophosphorous compounds.

A quantitative structure-property relationship analysis was performed on a series of 43 pesticides using multiple linear regression and support vector machines methods, which correlate the octanol-water partition coefficient (K_ow) values of these chemicals to their structural descriptors. At first, the data set was randomly separated into a training set (34 chemicals) and a test set (nine chemicals) for statistical external validation.

Models with three descriptors were developed using theoretical descriptors as independent variables derived from Dragon software while applying genetic algorithm-variable subset selection procedure.

The robustness and the predictive performance of the proposed linear model were verified using both internal and external statistical validation. One influential point which reinforces the model and an outlier were highlighted.

The purpose of this paper is to predict the retention times of 84 pesticides or toxicants.

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).

A model with six descriptors was developed using as independent variables. Theoretical descriptors derived from Spartan and Dragon softwares when applying GA/MLR approach.

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.

The purpose of this paper is to predict the aquatic toxicity (LC50) of 92 substituted benzenes derivatives in Pimephales promelas.

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.

Models with six descriptors were developed using as independent variables theoretical descriptors derived from Dragon software when applying genetic algorithm – variable subset selection procedure.

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.