Qspr Models for The Prediction of Octanol/water Partition Coefficient of Organophosphorous Insecticides | ||
| Egyptian Journal of Chemistry | ||
| Article 1, Volume 62, Issue 9, September 2019, Pages 1563-1574 PDF (1.23 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ejchem.2019.4976.1446 | ||
| Authors | ||
| Rana Amiri1; Djelloul Messadi* 2; Amel Bouakkadia3; leila lourici4 | ||
| 1Environmental and Food Safety Laboratory, Badji Mokhtar University, Annaba 23000, Algeria. | ||
| 2Environmental and Food Safety Laboratory, Badji Mokhtar University, Annaba 23000, Algeria. | ||
| 3Departement of chemistry Abbes Laghrour University, Khenchela 1252, Algeria | ||
| 4Departement of chemistry Chadli Bendjedid University, Eltaref 36000, Algeria. | ||
| Abstract | ||
| This study aims to predict the octanol/water partition coefficient (Kow) of 43 organophosphorous insecticides. Quantitative structure- property relationship analysis was performed on a series of 43 insecticides using Multiple Linear Regression (MLR) and Support Vector Machines (SVM) methods, which correlate octanol- water partition coefficient (Kow) values of these chemicals to their structural descriptors. At first, the data set was separated with duplex algorithm into a training set (22 chemicals) and a test set (21 chemicals) for statistical external validation. The IX'XI ratio for the two data sets was 0.9839 indicating that the volumes of the regions covered by the two data sets were approximately the same. Model with four descriptors was developed using as independent variables theoretical descriptors derived from DRAGON software when applying GA (Genetic Algorithm)- VSS (Variable Subset Selection) procedure . The values of statistical parameters R2, Q2ext, SDEPext and SDEC for MLR and SVM model were: (93.57%; 92.73%; 0.493; 0.463), (98.60%; 96.30%; 0.504; 0.316); obtained for the two approaches are very similar, which confirm that our four parameters model is stable, robust and significant. | ||
| Keywords | ||
| octanol/water partition coefficient; quantitative structure-property/activity relationship; Molecular descriptor; modeling; Support Vector Machine; multiple linear regression | ||
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