Quantitative structure-property relationship studies for prediction vapor pressure of volatile organic compounds

Mounia Zine, Amel Bouakkadia, Leila Lourici, Djelloul Messadi

Abstract


A theoretical model (QSPR) using multiple linear regression analysis for predicting vapor pressure (pv) of volatile organic compounds (VOCs) has been developed, a series of 51compounds were analyzed by multiple linear regression analysis. At first, the data set was separated arbitrarily into a training set (39 chemicals) and a test set (12 chemicals) for statistical external validation. The four-dimensional Model was developed using as independent variables theoretical descriptors derived from DRAGON software when applying GA (Genetic Algorithm)-VSS (Variable Subset Selection) procedure. The obtained model was used to predict the vapor pressure of test set compounds, and an agreement between experimental and predicted values was verified. This model, with high statistical significance (R2 = 0.9090, Q2LOO = 0.8748, Q2ext = 0.8307, s = 0.24), could be used adequately for the prediction and description of the log pv of other VOCs. The applicability domain of MLR models was investigated using William‘s plot to detect outliers and outsides compounds.


Keywords


Molecular descriptors; VOCs; log pv; Multiple Linear Regression

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References


V. Cirimele, M. Etter, M. Villian, P. Kintez, Ann. Toxicol. Anal. 20 (2008) 67 (https://doi.org/10.1051/ata/2009002)

S. Chtita, R. Hmamouchi, M. Larif, M. Ghamali, M. Bouachrine, T. Lakhlifi, J. Taibah. Univ. Sci. 10 (2016) 868 (https://doi.org/10.1016/j.jtusci.2015.04.007)

J. Akbar, S. Iqbal, F. Batool, A. Karim , K. W. Chan, Int. J. Mol. Sci. 13 (2012) 15387 (https://doi.org/10.3390/ijms131115387)

D. Mackay, W. Y. Shiu, K. C. Ma, S. C. Lee, Handbook of physical-chemical properties and environmental fate for organic chemicals Second edition, CRC Press Inc, Boca Raton, USA, 2006 (https://doi.org/10.1201/9781420044393)

HyperChem 6.03 Package. Hypercube, Inc., Gainesville, Florida, USA, 1999; software available at: (http://www.hyper.com)

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

R. Leardi, R. Boggia, M. Terrile, J. Chemom. 6 (1992) 267 (https://doi.org/10.1002/cem.1180060506)

R. Todeschini, D. Ballabio, V. Consonni, A. Mauri, M. Pavan, MOBYDIGS, Software for Multilinear Regression Analysis and Variable Subset Selection by Genetic Algorithm. Release 1.1 for windows, Milano, Italy, 2009 (http://www.talete.mi.it/)

L. Eriksson, J. Jaworska, A. Worth, M. Mc. Cronin, R. M. Dowell, P. Gramatica, Environ. Health. Perspect. 111 (2003) 1361 (https://doi.org/10.1289/ehp.5758)

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

H. Kubinyi, F. A. Hamprecht, T. Mietzner, J. Med. Chem. 41 (1998) 2553 (https://doi.org/10.1021/jm970732a)

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

L. F. Ramsey, W. D. Schafer, The Statistical Sleuth. Wadsworth Publishing Company, U.S.A. 1997 (https://ir.library.oregonstate.edu/downloads/j3860c13r)

A. J. Holder, D. M. Yourtee, D. A. White, A. G. Galaros, R. J. Smith Chain, J. Comput. Aided. Mol. Des. 17 (2003) 223 (https://doi.org/10.1023/A:1025382226037)

15. S. Chatterjee, A. S. Hadi, B. Price, Analysis of collinear data. In: Regression analysis by example, fourth ed. Wiley, New York, 2006, P. 221-258 (https://doi.org/10.1002/0470055464.ch9)

A. Tropsha, A. Golbraikh, Curr. Pharm. Des. 13 (2007) 3494 (https://doi.org/10.2174/138161207782794257)

W. Li, Y. Tang, Y. L. Zheng, Z. B. Qiu, Bioorg. Med. Chem. 14 (2006) 601 (https://doi.org/10.1016/j.bmc.2005.08.052)

R. Guha, D. T. Stanton, P. C. Jurs, J. Chem. Inf. Model. 45 (2005) 1109 (https://pubs.acs.org/doi/abs/10.1021/ci050110v)




DOI: https://doi.org/10.2298/JSC190306059Z

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