QSAR Study of the octanol/water partition coefficient of organophosphorus compounds: The hybrid GA/MLR and GA/ANN approaches

Main Article Content

Rana Amiri
Djelloul Messadi
Amel Bouakkadia

Abstract

This study aimed at predicting the n-octanol/water partition coef­ficient (Kow) of 43 organophosphorous insecticides. Quantitative structure–
–property rela­tionship analysis was performed on the series of 43 insecticides using two different methods, linear (multiple linear regression, MLR) and non-
-linear (artificial neural network, ANN), which Kow values of these chemicals to their struc­tural descriptors. First, the data set was separated with a duplex algorithm into a training set (28 chemicals) and a test set (15 che­mi­cals) for statistical external validation. A model with four descriptors was developed using as independent variables theoretical descriptors derived from Dragon software when applying genetic algorithm (GA)–variable subset selection (VSS) procedure. The values of statistical parameters, R2, Q2ext, SDEPext and SDEC for the MLR (94.09 %, 92.43 %, 0.533 and 0.471, respectively) and ANN model (97.24 %, 92.17 %, 0.466 and 0.332, respectively) obtained for the three approaches are very similar, which confirmed that the employed four parameters model is stable, robust and significant.

Article Details

How to Cite
[1]
R. Amiri, D. Messadi, and A. Bouakkadia, “QSAR Study of the octanol/water partition coefficient of organophosphorus compounds: The hybrid GA/MLR and GA/ANN approaches”, J. Serb. Chem. Soc., vol. 85, no. 4, pp. 467–480, Apr. 2020.
Section
Theoretical Chemistry

References

S. P. Bradbury, Tox. Lett. 79 (1995) 37(https://doi.org/10.1002/etc.5620160629)

A. P. Bearden, T. W. Schultz, Environ. Toxicol. Chem.16 (1997) 7

(https://doi.org/10.1016/0378-4274(95)03374-T)

X. J. Yao, M. C. Liu, X. Y. Zhang, Z. D. Hu, B. T. Fan, Anal. Chim. Acta 462 (2002) 101 (https://doi.org/10.1016/S0003-2670(02)00273-8)

H. Si, S. Yuan, K. Zhang, A. Fu, Y. B. Duan, Z. Hu, Chemometr. Intell. Lab. Syst. 90 (2008) 15 (https://doi.org/10.1016/j.chemolab.2007.06.011)

H. Y. Xu, J. Y. Zhang, J. W. Zou, X. S. Chen, J. Mol. Graph. Model. 26 (2008) 1076 (https://doi.org/10.1016/j.jmgm.2007.09.004)

F. Wania, D. Mackay, Environ. Pollut. 100 (1999) 223 (https://doi.org/10.1016/S0269-7491(99)00093-7)

S. W. Fisher, M. J. Lydy, J. Barger, P. F. Landrum, Environ. Toxicol. Chem. 12 (1993) 1307 (https://doi.org/10.1002/etc.5620120721)

C. J. Van Leeuwen, P. T. Vanderzandt, T. Aldenberg, H. J. M. Verhaar, J. L. M. Hermens, Environ. Toxicol. Chem. 11 (1992) 267 (https://doi.org/10.1002/etc.5620110216)

S. P. Niculescu, K. L. E. Kaiser, G. Schüürmann, Water Qual. Res. J. Can. 33 (1998) 153

(https://doi.org/10.2166/wqrj.1998.009)

S. L. Wong, Aquat. Toxicol. 6 (1985) 115 (https://doi.org/10.1016/0166-445X(85)90011-6)

M. Chessels, D. W. Hawker, D. W. Connell, Chemosphere 22 (1991) 1175 (https://doi.org/10.1016/0045-6535(91)90213-W)

R. Todeshini, V. Consonni, M. Pavan, Dragon, Software for the Calculation of Molecular Descriptors, Release 5.3 for Windows, Milan, 2006

T. Ferrari, A. Lombardo, E. Benfenati, Sci. Total Environ. 637 (2018) 1158 (https://doi.org/10.1016/j.scitotenv.2018.05.072)

A. Bouakkadia, L. Lourici, D. Messadi, Manage. Environ. Qual. 28 (2017) 579 (http://dx.doi.org/10.1108/MEQ-08-2015-0162)

H. Feng, Y. Chen, W. Yue, C Feng, Earth Environ. Sci. 153 (2018) 022035 (https://doi:10.1088/1755-1315/153/2/022035)

O. C. Hansen, Quantitative Structure-Activity Relationships (QSAR) and Pesticides, Teknologisk Institute, Pesticides Research No. 94 (2004)

HyperchemTM, Release 6.02 for Windows. Molecular Modeling system, 2000 (http://www.hyper.com/)

R. Todeschini, V. Consonni, A. Mauri, M. Pavan, Dragon Software, version 5.4, Copyright TALETE srl, 2005

R. D. Snee, Technometrics 19 (1977) 415 (https://doi.org/10.1080/00401706.1977.10489581)

R. Todeschini, D. Ballabio, V. Consonni, A. Mauri, M. Paven, MobyDigs, version 1.1, Copyright TALETE srl, 2009 (http://www.talete.mi.it/)

R. Leardi, R. Boggia, M. Tarrile, J. Chemom. 6 (1992) 267(https://doi.org/10.1016/B978-012213810-2/50004-9)

J. Xu, H. Zhang, L. Wang, G. Liang, L. Wang, X. Shen, W. Xu, Spectrochim. Acta, A 76 (2010) 239 (https://doi.org/10.1016/j.saa.2010.03.027)

R. Todeschini, A. Maiocchi, V. Consonni, Chemom. Int. Lab. Syst. 46 (1999) 13 (https://doi.org/10.1016/S0169-7439(98)00124-5)

A. Tropsha, P. Gramatica, V. K. Grombar, QSAR Comb. Sci. 22 (2003) 69 (https://doi.org/10.1002/qsar.200390007)

M. Shen, C. Béguin, A. Golbraikh, J. P. Stables, H. Kohn, A. Tropsha, J. Med. Chem. 47 (2004) 2356 (https://pubs.acs.org/doi/abs/10.1021/jm030584q)

S. Weisberg, Applied Linear Regression, 3rded., John Wiley and Sons, Inc., Hoboken, NJ, 2005

SCAN Software for Chemometric Analysis, version 1.1 for Windows, Minitab, USA 1995

J. Zupan, J. Gasteiger, Neural Networks in Chemistry and Drug Design, Wiley-VCH, Weinheim, 1999

A. Golbraikh, A. Tropsha, J. Mol. Graphics Modell. 20 (2002) 269 (https://doi.org/10.1016/S1093-3263(01)00123-1)

S. R. Gunn, Support Vector Machines for Classification and Regression, Technical Report, University of Southampton, 1998 (http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf)

N. Chen, W. Lu, J. Yang, G. Li, Support Vector Machine in Chemistry, World Scientific Publishing Co. Pte. Ltd, Singapore, 2004.