A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds

Qi Xu, Lingling Fan, Jie Xu

Abstract


A quantitative structure-property relationship (QSPR) analysis of the Setschenow constants (Ksalt) of organic compounds in a sodium chloride solution was carried out using only two-dimensional (2D) descriptors as input parameters. The whole set of 101 compounds was split into a training set of 71 compounds and a validation set of 30 compounds by means of the Kennard and Stones algorithm. A general four-parameter equation, with correlation coefficient (R) of 0.887 and standard error of estimation (s) of 0.031, was obtained by stepwise multilinear regression analysis (MLRA) on the training set. The reliability and robustness of the present model was verified with leave-one-out cross-validation, randomization tests, and the external validation set. All of the descriptors contained in this model are calculated directly from the molecular 2D structures; thus, this model can be used to easily predict the Ksalt of other compounds not involved in the present dataset.


Keywords


QSPR; Setschenow constants; 2D descriptor; multilinear regression analysis

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References


B. E. Conway, J. E. Desnoyers, A. C. Smith, On the Hydration of Simple Ions and Polyions, Philos. Trans. R. Soc. 131, 389–437 (1964).

W. L. Masterton, T. P. Lee, Salting coefficients from scaled particle theory, J. Phys. Chem. 74, 1776–1782 (1970).

S. Miyazaki, M. Oshiba, T. Nadai, Precaution on use of hydrochloride salts in pharmaceutical formulation, J. Pharm. Sci. 70, 594–596 (1981).

P. L. Gould, Salt Selection for Basic Drugs, Int. J. Pharm. 33, 201–217 (1986).

N. Ni, M. M. El-Sayed, T. Sanghvi, S. H. Yalkowsky, Estimation of the effect of NaCl on the solubility of organic compounds in aqueous solutions, J. Pharm. Sci., 1620–1625 (2000).

N. Ni, S. H. Yalkowsky, Prediction of Setschenow constants, Int. J. Pharm. 254, 167-172 (2003).

X. J. Yao, Y. W. Wang, X. Y. Zhang, R. S. Zhang, M. C. Liu, Z. D. Hu, B. T. Fan, Radial basis function neural network-based QSPR for the prediction of critical tem-perature, Chemom. Intell. Lab. Syst. 62, 217–225 (2002).

J. Xu, B. Guo, B. Chen, Q. Zhang, A QSPR treatment for the thermal stabilities of second-order NLO chromophore molecules, J. Mol. Model. 12, 65–75 (2005).

J. Xu, L. Liu, W. Xu, S. Zhao, D. Zuo, A general QSPR model for the prediction of θ(lower critical solution temperature) in polymer solutions with topological indices, J. Mol. Graph. Model. 26, 352–359 (2007).

J. Xu, H. Liang, B. Chen, W. Xu, X. Shen, H. Liu, Linear and nonlinear QSPR models to predict refractive indices of polymers from cyclic dimer structures, Chemom. Intell. Lab. Syst. 92, 152–156 (2008).

J. Xu, H. Zhang, L. Wang, W. Ye, W. Xu, Z. Li, QSPR analysis of infinite dilution activity coefficients of chlorinated organic compounds in water, Fluid Phase Equilib. 291, 111–116 (2010).

G. Liang, J. Xu, L. Liu, QSPR analysis for melting point of fatty acids using genetic algorithm based multiple linear regression (GA-MLR), Fluid Phase Equil. 353, 15–21 (2013).

X. Wang, Y. Sun, L. Wu, S. Gu, R. Liu, L. Liu, X. Liu, J. Xu, Quantitative structure-affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network, Chemom. Intell. Lab. Syst. 134, 1–9 (2014).

D. Wang, Y. Yuan, S. Duan, R. Liu, S. Gu, S. Zhao, L. Liu, J. Xu, QSPR study on melting point of carbocyclic nitroaromatic compounds by multiple linear regression and artificial neural network, Chemom. Intell. Lab. Syst. 143, 7–15 (2015).

Y. Yuan, Y. Sun, D. Wang, R. Liu, S. Gu, G. Liang, J. Xu, Quantitative structure-property relationship study of liquid vapor pressures for polychlorinated diphenyl ethers, Fluid Phase Equil. 391, 31–38 (2015).

X. Yu, B. Yi, X. Wang, Prediction of refractive index of vinyl polymers by using density functional theory, J. Comput. Chem. 28, 2336–2341 (2007).

X. Yu, Support Vector Machine-based QSPR for the Prediction of Glass Transition Temperatures of Polymers, Fiber. Polym. 11, 757–766 (2010).

X. Yu, R. Yu, Setschenow constant prediction based on the IEF-PCM calculations, Ind. Eng. Chem. Res. 52, 11182–11188 (2013).

A. Afantitis, G. Melagraki, K. Makridima, A. Alexandridis, H. Sarimveis, O. Iglessi-Markopoulou, Prediction of high weight polymers glass transition temperature using RBF neural networks, J. Mol. Struc. Theochem 716, 193–198 (2005).

A. Afantitis, G. Melagraki, H. Sarimveis, P. A. Koutentis, J. Markopoulos, O. Igglessi-Markopoulou, Prediction of intrinsic viscosity in polymer-solvent combinations using a QSPR model, Polymer 47, 3240–3248 (2006).

G. Melagraki, A. Afantitis, Enalos KNIME nodes: exploring corrosion inhibition of steel in acidic medium, Chemom. Intell. Lab. Syst. 123, 9–14 (2013).

Y. Li, Q. Hu, C. Zhong, Topological modeling of the Setschenow constant, Ind. Eng. Chem. Res. 43, 4465–4468 (2004).

J. Xu, L. Wang, L. Wang, G. Liang, X. Shen, W. Xu, Prediction of Setschenow constants of organic com-pounds based on a 3D structure representation, Chemom. Intell. Lab. Syst. 107, 178–184 (2011).

J. Xu, L. Wang, L. Wang, X. Shen, W. Xu, QSPR Study of Setschenow Constants of Organic Compounds Using MLR, ANN, and SVM Analyses, J. Comput. Chem. 32, 3241–3252 (2011).

M. Shen, A. LeTiran, Y. Xiao, A. Golbraikh, H. Kohn, A. Tropsha, Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods, J. Med. Chem. 45, 2811–2823 (2002).

D. Rogers, A. J. Hopfinger, Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships, J. Chem. Inf. Comput. Sci. 34, 854–866 (1994).

R. W. Kennard, L. A. Stone, Computer aided design of experiments, Technometrics 11, 137–148 (1969).

A. Tropsha, P. Gramatica, V. K. Gombar, The Im-portance of Being Earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22, 69–77 (2003).

W. Wu, B. Walczak, D. L. Massart, S. Heuerding, F. Erni, I. R. Last, K. A. Prebble, Artificial neural networks in classification of NIR spectral data: Design of the training set, Chemom. Intell. Lab. Syst. 33, 35–46 (1996).

C. T. Klein, D. Polheim, H. Viernstein, P. Wolschann, Predicting the free energies of complexation between cyclodextrins and guest molecules: linear versus nonlinear models, Pharm. Res. 17, 358–365 (2000).

R. Todeschini, V. Consonni, A. Mauri, M. Pavan. TALETE srl, Milan, 2006.

H. Liu, P. Gramatica, QSAR study of selective ligands for the thyroid hormone receptor β, Bioorgan. Med. Chem. 15, 5251–5261 (2007).

A. J. Holder, D. M. Yourtee, D. A. White, A. G. Glaros, R. Smith, Chain melting temperature estimation for phosphatidyl cholines by quantum mechanically derived quantitative structure property relationships, J. Comput. Aid. Mol. Des. 17, 223–230 (2003).

A. Golbraikh, A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20, 269–276 (2002).

M. Shen, C. Béguin, A. Golbraikh, J. P. Stables, H. Kohn, A. Tropsha, Application of predictive QSAR models to database mining: Identification and experimental validation of novel anticonvulsant compounds, J. Med. Chem. 47, 2356–2364 (2004).

J. Xu, B. Chen, Q. Zhang, B. Guo, Prediction of refractive indices of linear polymers by a four-descriptor QSPR model, Polymer 45, 8651–8659 (2004).

F. Zheng, E. Bayram, S. P. Sumithran, J. T. Ayers, C.-G. Zhan, J. D. Schmitt, L. P. Dwoskin, P. A. Crooks, QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release, Bioorg. Med. Chem. 14, 3017–3037 (2006).

R. Guha, P. C. Jurs, Interpreting computational neural network QSAR models: A measure of descriptor im-portance, J. Chem. inf. Model. 45, 800–806 (2005).




DOI: http://dx.doi.org/10.20450/mjcce.2016.848

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