QSPR Models for the Prediction of Some Thermodynamic Properties of Cycloalkanes Using GA-MLR Method

Daryoush       Joudaki; Fatemeh       Shafiei

doi:10.2174/1573409915666191028110756

Abstract

Aim and Objective: Cycloalkanes have been largely used in the field of medicine, components of food, pharmaceutical drugs, and they are mainly used to produce fuel.

In present study the relationship between molecular descriptors and thermodynamic properties such as the standard enthalpies of formation (∆H°_f), the standard enthalpies of fusion (∆H°_fus), and the standard Gibbs free energy of formation (∆G°_f)of the cycloalkanes is represented.

Materials and Methods: The Genetic Algorithm (GA) and multiple linear regressions (MLR) were successfully used to predict the thermodynamic properties of cycloalkanes. A large number of molecular descriptors were obtained with the Dragon program. The Genetic algorithm and backward method were used to reduce and select suitable descriptors.

Results: QSPR models were used to delineate the important descriptors responsible for the properties of the studied cycloalkanes. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF), Pearson Correlation Coefficient (PCC) and the Durbin–Watson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The statistical parameters of the training, and test sets for GA–MLR models were calculated.

Conclusion: The results of the present study indicate that the predictive ability of the models was satisfactory and molecular descriptors such as: the Functional group counts, Topological indices, GETAWAY descriptors, Constitutional indices, and molecular properties provide a promising route for developing highly correlated QSPR models for prediction the studied properties.

Keywords: Cycloalkanes, structure -property relationship, enthalpies of formation, enthalpies of fusion, Gibbs free energy of formation, genetic algorithm -multiple linear regressions (GA-MLR).

« Previous Next »

Graphical Abstract

[1] 
Devillers, J.; Balaban, A.T. Topological Indices and Related Descriptors in QSAR and QSPR; Gordon and Breadch Science Pub.: Amsterdam, Netherland, 1999. 
[2] 
Verma, J.; Khedkar, V.M.; Coutinho, E.C. 3D-QSAR in drug design--a review. Curr. Top. Med. Chem.,  2010, 10(1), 95-115.
[http://dx.doi.org/10.2174/156802610790232260] [PMID:  19929826] 
[3] 
Diudea, M.V. QSAR/QSPR studies by Molecular Descriptors; Nova Pub.: Huntington, New York, 2001. 
[4] 
Hessler, G.; Baringhaus, K.H. Artificial Intelligence in Drug Design. Molecules,  2018, 23(10), 2520-2533.
[http://dx.doi.org/10.3390/molecules23102520] [PMID:  30279331] 
[5] 
Ali, M.; Patel, M.; Wilkinson, D.; Judson, P.; Cross, K.; Bower, D. ToxML, a data exchange standard with content controlled vocabulary used to build better (Q)SAR models. SAR QSAR Environ. Res.,  2013, 24(6), 429-438.
[http://dx.doi.org/10.1080/1062936X.2013.783506] [PMID:  23621552] 
[6] 
Nigam, A.; Klein, M.T. A mechanism-oriented lumping strategy for heavy hydrocarbon pyrolysis: imposition of quantitative structure-reactivity relationships for pure components. Ind. Eng. Chem. Res.,  1993, 32, 1297-1303.
[http://dx.doi.org/10.1021/ie00019a003] 
[7] 
Kubinyi, H.; Folkers, G.; Martin, Y.C. Similarity and dissimilarity: a medicinal chemist’s view. 3D QSAR in Drug Design 2002, 7(2), 225-252.
[8] 
Kubinyi, H. QSAR and 3D QSAR in drug design part 2: Applications and problems. Drug Discov. Today,  1997, 2(12), 538-546.
[http://dx.doi.org/10.1016/S1359-6446(97)01084-2] 
[9] 
Perkins, R.; Fang, H.; Tong, W.; Welsh, W.J. Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology. Environ. Toxicol. Chem.,  2003, 22(8), 1666-1679.
[http://dx.doi.org/10.1897/01-171] [PMID:  12924569] 
[10] 
Sahoo, S.; Adhikari, C.; Kuanar, M.; Mishra, B.K. A short review of the generation of molecular descriptors and their applications in quantitative structure property/activity relationships. Curr. Comput-Aid. Curr. Comput. Aided Drug Des.,  2016, 12(3), 181-205.
[http://dx.doi.org/10.2174/1573409912666160525112114] [PMID:  27222031] 
[11] 
Mercader, A.G.; Duchowicz, P.R.; Fernández, F.M.; Castro, E.A. Modified and enhanced replacement method for the selection of molecular descriptors in QSAR and QSPR theories. Chemom. Intell. Lab. Syst.,  2008, 92, 138-144.
[http://dx.doi.org/10.1016/j.chemolab.2008.02.005] 
[12] 
Shafiei, F.; Arjmand, F. Prediction of the normal boiling points and enthalpy of vaporizations of alcohols and phenols using topological. J. Struct. Chem.,  2018, 59, 748-754.
[http://dx.doi.org/10.1134/S0022476618030393] 
[13] 
Todeschini, R.; Consonni, V. Handbook of molecular descriptors; Wiley-VCH: Weinheim, 2000. 
[http://dx.doi.org/10.1002/9783527613106] 
[14] 
Grisoni, F.; Ballabio, D.; Todeschini, R.; Consonni, V. Molecular descriptors for structure-activity applications: a hands-on approach. Methods Mol. Biol.,  2018, 1800, 3-53.
[http://dx.doi.org/10.1007/978-1-4939-7899-1_1] [PMID:  29934886] 
[15] 
Pourbasheer, E.; Ahmadpour, S.; Zare-Dorabei, R.; Nekoei, M.M. Quantitative structure activity relationship study of p38a MAP kinase inhibitors. Arab. J. Chem.,  2017, 10, 33-40.
[http://dx.doi.org/10.1016/j.arabjc.2013.05.009] 
[16] 
Cao, C.; Yuan, H. Topological indices based on vertex, distance, and ring: on the boiling points of paraffins and cycloalkanes. J. Chem. Inf. Comput. Sci.,  2001, 41(4), 867-877.
[http://dx.doi.org/10.1021/ci000467t] [PMID:  11500103] 
[17] 
Ponce, Y.M. Total and local quadratic indices of the molecular pseudograph’s atom adjacency matrix: applications to the prediction of physical properties of organic compounds. Molecules,  2003, 8, 687-726.
[http://dx.doi.org/10.3390/80900687] 
[18] 
Tomović, Z.; Gutman, I. Modeling boiling points of cycloalkanes by means of iterated line graph sequences. J. Chem. Inf. Comput. Sci.,  2001, 41(4), 1041-1045.
[http://dx.doi.org/10.1021/ci010006n] [PMID:  11500122] 
[19] 
Toropov, A.A.; Nesterov, I.V.; Nabiev, O.M. QSPR modeling of cycloalkanes properties by correlation weighting of extended graph valence shells. J. Mol. Struc- THEOCHEM.,  2003, 637(1-3), 37-42.
[20] 
Gao, W.; Chen, Y.; Wang, W. The topological variable computation for a special type of cycloalkanes. J. Chem.,  2017, 2017, 1-8.
[http://dx.doi.org/10.1155/2017/6534758] 
[21] 
Katritzky, A.R.; Slavov, S.H.; Stoyanova-Slavova, I.S.; Kahn, I.; Karelson, M. Quantitative structure-activity relationship (QSAR) modeling of EC50 of aquatic toxicities for Daphnia magna. J. Toxicol. Environ. Health A,  2009, 72(19), 1181-1190.
[http://dx.doi.org/10.1080/15287390903091863] [PMID:  20077186] 
[22] 
Wang, Z.Y.; Zhai, Z.C.; Wang, L.S. Quantitative Structure‐activity Relationship of Toxicity of Alkyl (1‐phenylsulfonyl) Cycloalkane‐carboxylates Using MLSER Model and Ab initio. QSAR Comb. Sci.,  2005, 24(2), 211-217.
[http://dx.doi.org/10.1002/qsar.200430873] 
[23] 
Smolenskii, E.A.; Ryzhov, A.N.; Bavykin, V.M.; Myshenkova, T.N.; Lapidus, A.L. Octane numbers (ONs) of hydrocarbons: a QSPR study using optimal topological indices for the topological equivalents of the ONs. Russ. Chem. Bull.,  2007, 56(9), 1681-1693.
[http://dx.doi.org/10.1007/s11172-007-0262-2] 
[24] 
Mohajeri, A.; Manshour, P.; Mousaee, M. A novel topological descriptor based on the expanded wiener index: Applications to QSPR/QSAR studies. Iranian J. Math. Chem.,  2017, 8(2), 107-135.
[25] 
Stokes, R.H.; Marsh, K.N.; Tomlins, R.P. Enthalpies of exothermic mixing Enthalpies of exothermic mixing measured by the isothermal displacement calorimeter for cyclo-octane + cyclopentane at 25 °C. J. Chem. Thermodyn.,  1969, 1, 377-379.
[http://dx.doi.org/10.1016/0021-9614(69)90067-6] 
[26] 
Ewing, M.B.; Marsh, K.N. Thermodynamics of cycloalkane+cycloalkane mixtures: comparison with theory. J. Chem. Thermodyn.,  1977, 9, 863-871.
[http://dx.doi.org/10.1016/0021-9614(77)90172-0] 
[27] 
Olariu, T.; Vlaia, V.; Ciubotariu, C.; Dragos, D.; Ciubotariu, D.; Mracec, M. Quantitative relationships for the prediction of the vapor pressure of some hydrocarbons from the van der Waals molecular surface. J. Serb. Chem. Soc.,  2015, 80, 659-671.
[http://dx.doi.org/10.2298/JSC140416051O] 
[28] 
Cao, C.; Yuan, H. A Modified distance matrix to distinguish cis/trans isomers of cycloalkanes, internet. Electron. J. Mol. Des.,  2002, 1, 401-409.
[29] 
Fjodorova, N.; Novič, M. Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity. Comput. Struct. Biotechnol. J.,  2012, 1(2),e201207003.
[http://dx.doi.org/10.5936/csbj.201207003] [PMID:  24688639] 
[30] 
Róg, G. Kozłowska-Róg, A.; Dudek, M. The standard Gibbs free energy of formation of calcium chromium (III) oxide in the temperature range (1073 to 1273). K. J. Chem. Thermodyn.,  2007, 39(2), 275-278.
[http://dx.doi.org/10.1016/j.jct.2006.07.005] 
[31] 
Jaramillo, D.; Plascencia, G. Basic Thermochemistry in Materials Processing; Springer International Publishing, 2017. 
[32] 
Atkins, P.; Julio De, P.; Keeler, P. Physical Chemistry, 11th ed; Oxford University Press, 2018. 
[33] 
Dohoo, I.R.; Ducrot, C.; Fourichon, C.; Donald, A.; Hurnik, D. An overview of techniques for dealing with large numbers of independent variables in epidemiologic studies. Prev. Vet. Med.,  1997, 29(3), 221-239.
[http://dx.doi.org/10.1016/S0167-5877(96)01074-4] [PMID:  9234406] 
[34] 
González, M.P.; Terán, C.; Saíz-Urra, L.; Teijeira, M. Variable selection methods in QSAR: an overview. Curr. Top. Med. Chem.,  2008, 8(18), 1606-1627.
[http://dx.doi.org/10.2174/156802608786786552] [PMID:  19075770] 
[35] 
Lučić, B.; Trinajstić, N. New Developments in QSPR/QSAR Modeling Based on Topological Indices. SAR QSAR Environ. Res.,  1997, 7, 45-62.
[http://dx.doi.org/10.1080/10629369708039124] 
[36] 
Kapur, G.S.; Ecker, A.; Meusinger, R. Establishing quantitative structure-property relationships (QSPR) of diesel samples by proton-NMR & multiple linear regression(MLR) analysis, ‎. Energy Fuels,  2001, 15, 943-948.
[http://dx.doi.org/10.1021/ef010021u] 
[37] 
Yin, C.; Liu, X.; Guo, W.; Lin, T.; Wang, X.; Wang, L. Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors. Water Res.,  2002, 36(12), 2975-2982.
[http://dx.doi.org/10.1016/S0043-1354(01)00532-2] [PMID:  12171394] 
[38] 
Gramatica, P.; Pilutti, P.; Papa, E. Ranking of volatile organic compounds for tropospheric degradability by oxidants: a QSPR approach. SAR QSAR Environ. Res.,  2002, 13(7-8), 743-753.
[http://dx.doi.org/10.1080/1062936021000043472] [PMID:  12570050] 
[39] 
Roy, K.; Leonard, J.T. On selection of training and test sets for the development of predictive QSAR models. QSAR Comb. Sci.,  2006, 25, 235-251.
[http://dx.doi.org/10.1002/qsar.200510161] 
[40] 
Schüürmann, G.; Ebert, R.U.; Chen, J.; Wang, B.; Kühne, R. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean. J. Chem. Inf. Model.,  2008, 48(11), 2140-2145.
[http://dx.doi.org/10.1021/ci800253u] [PMID:  18954136] 
[41] 
Chatterje, S.; Hadi, A.S. Regression Analysis by Example, 2006.
[http://dx.doi.org/10.1002/0470055464] 
[42] 
Weisberg, S. Applied Linear Regression, 3rd ed; John & Sonc, Inc.: Hoboken, 2005. 
[http://dx.doi.org/10.1002/0471704091] 
[43] 
Hateka, N.R. Tests for Detecting Autocorrelation. Principles of Econometrics: An Introduction (Using R); SAGE Publications, 2010, pp. 379-382.
[http://dx.doi.org/10.4135/9781446270110] 
[44] 
Aptula, A.O.; Jeliazkova, N.G.; Schultz, T.W.; Cronin, M.T.D. The better predictive model: High q2 for the training set or low root mean square error of prediction for the test set? QSAR Comb. Sci.,  2005, 24, 385-396.
[http://dx.doi.org/10.1002/qsar.200430909] 
[45] 
Depiereux, E.; Vincke, G.; Dehertogh, B. Biostatistics, 2005.
[46] 
Yoo, W.; Mayberry, R.; Bae, S.; Singh, K.; Peter, Q. He.; Lillard, Jr. J.W. A Study of Effects of Multicollinearity in the Multivariable Analysis. Int. J. Appl. Sci. Technol.,  2014, 4, 9-19.
[PMID:  25664257] 
[47] 
Reisfeld, B.; Mayeno, A. N. Computational Toxicology: Volume 21, On the Development and Validation of QSAR Models, Springer: Science+Business Media, LLC, 2013, 499-529.
[48] 
Craney, T.A.; Surles, J.G. Model-Dependent Variance Inflation Factor Cutoff Values. Qual. Eng.,  2002, 14, 391-403.
[http://dx.doi.org/10.1081/QEN-120001878] 
[49] 
Tropsha, A.; Gramatica, P.; Gombar, V.K. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb. Sci.,  2003, 22, 69-77.
[http://dx.doi.org/10.1002/qsar.200390007] 
[50] 
Hawkins, D.M.; Kraker, J.J.; Basak, S.C.; Mills, D. QSPR checking and validation: a case study with hydroxy radical reaction rate constant. SAR QSAR Environ. Res.,  2008, 19(5-6), 525-539.
[http://dx.doi.org/10.1080/10629360802349058] [PMID:  18853300] 
[51] 
Benigni, R.; Bossa, C. Predictivity of QSAR. J. Chem. Inf. Model.,  2008, 48(5), 971-980.
[http://dx.doi.org/10.1021/ci8000088] [PMID:  18426198] 
[52] 
Kolossov, E.; Stanforth, R. The quality of QSAR models: problems and solutions. SAR QSAR Environ. Res.,  2007, 18(1-2), 89-100.
[http://dx.doi.org/10.1080/10629360601053984] [PMID:  17365961] 
[53] 
Roy, P.P.; Leonard, J.T.; Roy, K. Exploring the impact of the size of training sets for the development of predictive QSAR models. Chemom. Intell. Lab. Syst.,  2008, 90, 31-42.
[http://dx.doi.org/10.1016/j.chemolab.2007.07.004] 
[54] 
Konovalov, D.A.; Llewellyn, L.E.; Vander Heyden, Y.; Coomans, D. Robust cross-validation of linear regression QSAR models. J. Chem. Inf. Model.,  2008, 48(10), 2081-2094.
[http://dx.doi.org/10.1021/ci800209k] [PMID:  18826208] 
[55] 
Chatterjee, S.; Simonoff, J. Handbook of Regression Analysis; John Wiley & Sons: New York, 2013. 
[56] 
Consonni, V.; Todeschini, R.; Pavan, M.; Gramatica, P. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies. J. Chem. Inf. Comput. Sci.,  2002, 42(3), 693-705.
[http://dx.doi.org/10.1021/ci0155053] [PMID:  12086531] 
[57] 
Tatevskii, V.M. The Theory of Physicochemical Properties of Molecules and Substances; MSU Publishing House, 1987. 
[58] 
Benson, S.W.; Buss, J.H. Additivity rules for the estimation of molecular properties. Thermodynamic properties. J. Phys. Chem.,  1958, 29(3), 546-572.
[http://dx.doi.org/10.1063/1.1744539] 
[59] 
Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. Prediction of Hydrophilic (Lipophilic) Properties of Small Organic Molecules Using Fragmental Methods: An analysis of ALOGP and CLOGP Methods. J. Phys. Chem.,  1998, 102, 3762-3772.
[http://dx.doi.org/10.1021/jp980230o] 

Rights & Permissions Print Cite

Article Metrics

16

Journal Information

For Authors

For Editors

For Reviewers

Explore Articles

Open Access

Open Access Articles

For Visitors

DOI https://dx.doi.org/10.2174/1573409915666191028110756	Print ISSN 1573-4099
Publisher Name Bentham Science Publisher	Online ISSN 1875-6697

Current Computer-Aided Drug Design

QSPR Models for the Prediction of Some Thermodynamic Properties of Cycloalkanes Using GA-MLR Method

Abstract Play Pause

Graphical Abstract

Related Journals

Related Books

Abstract