QSAR Study of octanol/water partition coefficient of organophosphorous compounds: Hybrid (GA/ MLR) Approach and Hybrid (GA/ ANN)

Rana Amiri, Djelloul Messadi, Amel Bouakkadia


This study aims to predict the n-octanol/water partition coefficient (Kow) of 43 organophosphorous insecticides. Quantitative structure-property relationship analysis was performed on a series of 43 insecticides using twodifferents methods linear (Multiple Linear Regression)  and nonlinear (Artificial Neural Network), 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 (28 chemicals) and a test set (15 chemicals) for statistical external validation. 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 ANN model were: (94.09 %; 92.43%; 0.533; 0.471), (97.24%; 92.17%; 0.466; 0.332), obtained for the three approaches are very similar, which confirm that our four parameters model is stable, robust and significant.


octanol/water partition coefficient; molecular descriptors; QSPR methods

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DOI: https://doi.org/10.2298/JSC190610090A

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