Modeling of linear and nonlinear quantitative structure property relationships of the aqueous solubility of phenol derivatives

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Soumaya Kherouf
Nabil Bouarra
https://orcid.org/0000-0001-5438-8678
Amel Bouakkadia
Djelloul Messadi

Abstract

Quantitative structure–solubility relationships (QSSR) are considered as a type of Quantitative structure–property relationship (QSPR) study in which aqueous solubility of chemicals are related to chemical structure. In the present work, multiple linear regression (MLR) and artificial neural network (ANN) techniques were used for QSSR studies of the water solubility of 68 phenols (phenol and its derivatives) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and a genetic algorithm (GA), the descriptors that resulted in the best fitted models were selected. After descriptor selection, multiple linear regression (MLR) was used to construct a linear QSSR model. The R2 = 91.0 %, = 89.33 %, s = 0.340 values of the model developed by MLR showed a good predictive capability for log S values of phenol and its derivatives. The results of MLR model were compared with those of the ANN model. the comparison showed that the R2 = 94.99 %, s = 0.245 of ANN were higher and lower, respectively, which illustrated an ANN presents an excellent alternative to develop a QSSR model for the log S values of phenols to MLR.

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How to Cite
[1]
S. Kherouf, N. Bouarra, A. Bouakkadia, and D. Messadi, “Modeling of linear and nonlinear quantitative structure property relationships of the aqueous solubility of phenol derivatives”, J. Serb. Chem. Soc., vol. 84, no. 6, pp. 575–590, Jul. 2019.
Section
Theoretical Chemistry

References

J. Devillers, SAR QSAR Environ. Res. 15 (2004) 237. (https://doi.org/10.1080/10629360410001724905)

Y. B. Zang, Chin, Agric. Sci. Bull. 28 (2012) 282 (http://caod.oriprobe.com/articles/28599226/Research_Advance_of_Phenol_Adso-rption_of_Modified_Bentonite.htm)

P. R. Zhan, H. T. Wang, Z. X. Chen, J. Agro-Environ. Sci. 27 (2008) 801 (http://en.cnki.com.cn/Article_en/CJFDTotal-NHBH200802077.htm)

J. Micha Lowicz , R. Ożadowicz Wirgiliusz Duda, Water Air Soil Poll. 16 (2005) 205 (https://doi.org/10.1007/s11270-005-3022-7)

S. D. Palmer, N. M. OBoyle, R. C. Glen, J. B. O. Mitchell, J. Chem. Inf. Model. 47 (2007) 150 (https://pubs.acs.org/doi/abs/10.1021/ci060164k)

R. Gozalbes, A. Pineda-Lucena, Bioorg. Med. Chem. 18 (2010) 7078 (https://doi.org/10.1016/j.bmc.2010.08.003)

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)

M. St. J. Warne, D. W. Connel, D. W. Hawker, G. Schüürmann, Chemosphere 21 (1990) 877 (https://doi.org/10.1016/0045-6535(90)90168-S)

C. Catana, H. Gao, C. Orrenius, P. F. W. Stouten, J. Chem. Inf. Model. 45 (2005) 170 (https://doi.org/10.1021/ci049797u)

Z. Garkani-Nejad, M. Ahmadvand. Sep. Sci. Technol. 46 (2011) 1034 (http://doi.org/10.1080/01496395.2010.539587)

D. Mackay, W. Y. Shiu, K. C. Ma, S. C. Lee, Handbook of physical-chemical properties and environmental fate for organic chemicals, 2nd ed., CRC Press, Boca Raton, FL, 2006 (https://www.taylorfrancis.com/books/9781420044393)

E. Benfenati, J. R. Chrétien, G. Gini, N. Piclin, M. Pintore, A. Roncaglioni, in Quanti-tative Structure–Activity Relationships (QSAR) for Pesticide Regulatory Purposes, Elsevier, Amsterdam, 2007, p. 185–199. (https://doi.org/10.1016/B978-044452710-3/50008-2)

HyperChem 6.03 Package. Hypercube, Inc., Gainesville, FL, 1999; software available at: http://www.hyper.com

Talete Srl. Dragon for windows (Software for Molecular Descriptor Calculation), version 5.5, Milano, 2007; software available at: http://www.talete.mi.it

J. E. Jackson, A Users Guide to Principal Component, Wiley, New York, 1991 (https://doi.org/10.1002/0471725331)

R. Kennard, L. A. Stone, Technometrics 11 (1969) 137 (https://doi.org/10.1080/00401706.1969.10490666)

P. Gramatica, N. Chirico, E. Papa, S. Cassani, S. Kovarich, QSARINS, Software for the Development and validation of QSAR MLR Models, available on request at http://www.qsar.it

P. Gramatica, N. Chirico, E. Papa, S. Kovarich, S. Cassani, J. Comput. Chem. 34 (2013) 2121 (https://doi.org/10.1002/jcc.23361)

J. Zupan, J. Gasteiger, Neural Networks in Chemistry and Drug Design; Wiley–VCH, Weinheim, 1999 (https://www.wiley.com/en-us/Neural Networks in Chemistry and Drug Design%3A An Introduction%2C 2nd Edition-p-9783527297795)

S. Haykin, Neural Networks. A Comprehensive Foundation, Perarson Prentice Hall, New Delhi, 2006 (ISBN-13: 978-0023527616)

D. E. Rumelhart, G. E. Hinton, R. J. Williams, Nature 323 (1986) 33 (https://doi.org/10.1038/323533a0)

OECD. Guidance Document on the Validation of (Quantitative) Structure–Activity Relationships [(Q)SAR] Models, Organisation for Economic Co-Operation and Development, Paris, 2007 (https://oecd.org/chemicalsafety/risk-assessment/validationofqsarmodels.htm)

A. Tropsha, P. Gramatica, V. K. Gombar, QSAR Comb. Sci. 22 (2003) 70 (https://doi.org/10.1002/qsar.200390007)

N. Chirico, P. Gramatica, J. Chem. Inf. Model. 51 (2011) 2320 (https://doi.org/10.1021/ci200211n)

P. Gramatica, Mol. Inf, 33 (2014) 311 (https://doi.org/10.1002/minf.201400030)

G. Schüürmann, R. Ebert, J. Chen, B. Wang, R. Kühne, J. Chem. Inf. Model. 48 (2008) 2140 (https://doi.org/10.1021/ci800253u)

V. Consonni, D. Ballabio, R. Todeschini, J. Chem. Inf. Model. 49 (2009) 1669 (https://doi.org/10.1021/ci900115y)

V. Consonni, D. Ballabio, R. Todeschini, J. Chemom. 24 (2010) 194 (https://doi.org/10.1002/cem.1290)

L. I. Lin, Biometrics. 45(1989) 255 (https://doi.org/10.2307/2532051)

N. Chirico, P. Gramatica, J. Chem. Inf. Model. 51 (2011) 2320 (https://doi.org/10.1021/ci200211n)

T. I. Netzeva, A. P. Worth, T. Aldenberg, R. Benigni, M. T. D. Cronin, P. Gramatica, J. S. Jaworska, S. Kahn, G. Klopman, C. A. Marchant, G. Myatt, N. Nikolova-Jeliazkova, G. Y. Patlewicz, R. Perkins, D. W. Roberts, T. W. Schultz, D. T. Stanton, J. J. M. Van de Sandt, W. Tong, G. Veith, C. Yang, ATLA. Altern. Lab. Anim. 33 (2005) 155 (https://www.ncbi.nlm.nih.gov/pubmed/16180989)

N. Chirico, P. Gramatica. J. Chem. Inf. Model. 52 (2012) 2044 (https://doi.org/10.1021/ci300084j)

G. R. Famini, C. A. Penski, L. Y. Wilson, J. Phys. Org. Chem. 5 (1992) 395 (https://doi.org/10.1002/poc.610050704)

J. Xu, H. Liu, W. Li, H. Zou, W. Xu, Macromol. Theory Simul. 17 (2008) 470 (https://doi.org/10.1002/mats.200800063)

D. E. Rumelhart, G. E. Hinton, R. J. Williams, Nature 323 (1986) 33 (https://doi.org/10.1038/323533a0)

F. Zheng, E. Bayram, S. P. Sumithran, J. T. Ayers, C. Zhan, J. D. Schmitt, L. P. Dwoskin, P. A. Crooks, Bioorg. Med. Chem. 14 (2006) 3017 (https://doi.org/10.1016/j.bmc.2005.12.036)

R. Guha, P. C. Jurs, J. Chem. Inf. Model. 45 (2005) 800 (https://doi.org/10.1021/ci050022a).

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