Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides Scientific paper

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Published: Aug 1, 2021
DOI: https://doi.org/10.2298/JSC200618066B
Keywords:
genetic algorithm agrochemicals descriptors statistical methods

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Amel Bouakkadia
Environmental and Food Safety Laboratory, Department of Chemistry, Badji Mokhtar University - Annaba, BP. 12, 23000 Annaba, Algeria and Abbes Laghrour University, Faculty of Sciences and Technology - Khenchela, BP 1252 Route de Batna 40004 Khenchela, Algeria
Noureddine Kertiou
Environmental and Food Safety Laboratory, Department of Chemistry, Badji Mokhtar University - Annaba, BP. 12, 23000 Annaba, Algeria and Abbes Laghrour University, Faculty of Sciences and Technology - Khenchela, BP 1252 Route de Batna 40004 Khenchela, Algeria
Rana Amiri
Environmental and Food Safety Laboratory, Department of Chemistry, Badji Mokhtar University - Annaba, BP. 12, 23000 Annaba, Algeria
Youssouf Driouche
Environmental and Food Safety Laboratory, Department of Chemistry, Badji Mokhtar University - Annaba, BP. 12, 23000 Annaba, Algeria
https://orcid.org/0000-0001-8024-407X
Djelloul Messadi
Environmental and Food Safety Laboratory, Department of Chemistry, Badji Mokhtar University - Annaba, BP. 12, 23000 Annaba, Algeria

Abstract

The partitioning tendency of pesticides, in this study herbicides in particular, into different environmental compartments depends mainly of the physicochemical properties of the pesticides itself. Aqueous solubility (S) indi­cates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure for determining the aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, always associated with selection of a genetic algorithm (GA) of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series of 80 herbicides. The values of log S of the studied compounds were well correlated with the descriptors. Considering the pertinent descriptors, a Pearson correlation squared coefficient (R2) of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; σ = 0.1111 and ε = 0.222.

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[1]
A. Bouakkadia, N. Kertiou, R. Amiri, Y. Driouche, and D. Messadi, “Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides: Scientific paper”, J. Serb. Chem. Soc., vol. 86, no. 7-8, pp. 673–684, Aug. 2021.
Issue
Vol. 86 No. 7-8 (2021)
Section
Theoretical Chemistry
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