QSPR MODEL FOR PREDICTING OF THE STANDARD ENTHALPY OF FORMATION OF COMPLEX OXYGEN-CONTAINING INORGANIC COMPOUNDS
DOI:
https://doi.org/10.15407/dopovidi2024.01.050Keywords:
standard enthalpy of formation, simplex approach, 1D-simplex approach, quantitative structure-property relationship, partial least squaresAbstract
This study utilized a dataset comprising 74 oxygen-containing inorganic compounds, including complex mixed oxides and salts—some of which show promise as components of optical materials. Employing the 1D-simplex approach, a robust consensus QSPR model for the standard enthalpy of formation (ΔН0 298) was developed for the investigated compounds. The calculation of 1D simplex descriptors involved differentiating vertices (atoms) in simplexes based on various characteristics, including those from Mendeleev’s periodic system, oxidation state, electronegativity, ionic radius, and features of van der Waals interactions. The predictive QSPR model was constructed using the partial least squares (PLS) method. For the test set of the developed 1D-PLS model, the coefficient of determination was found to be 0.94, with an average relative error of 10.0%. An analysis of the influence of structural parameters on the standard enthalpy of formation for the studied compounds revealed that the orbital quantum numbers of electrons in the outer layer of atoms (28 %) and van der Waals interactions (19 %) had the most significant contributions. The developed model proves useful for the preliminary estimation of the standard enthalpy of formation for various oxygen-containing inorganic compounds.
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Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., Consonni, V., Kuz’min, V. E., Cramer, R., Benigni, R., Yang, C., Rathman, J., Ter- floth, L., Gasteiger, J., Richard, A. & Tropsha, A. (2014). QSAR modeling: where have you been? Where are you going to? J. Med. Chem., 57, No. 12, pp. 4977-5010. https://doi.org/10.1021/jm4004285
Wang, T., Yuan, X.-S., Wu, M.-B., Lin, J.-P. & Yang, L.-R. (2017). The advancement of multidimensional QSAR for novel drug discovery — where are we headed? Expert Opin. Drug Discov., 12, No. 8, pp. 769-784. https://doi.org/10.1080/17460441.2017.1336157
Kuz’min, V., Artemenko, A., Ognichenko, L., Hromov, A., Kosinskaya, A., Stelmakh, S., Sessions, Z. L. & Muratov, E. N. (2021). Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct. Chem., 32, No. 4, pp. 1365-1392. https://doi.org/10.1007/s11224-021-01793-z
Kuz’min, V., Artemenko, A., Оgnichenko, L., Muratov, E. & Hromov, A. (2022). Simplex representation of molecular structure. Saarbrücken: Lambert (in Russian).
Kuz’min, V. E., Ognichenko, L. N., Zinchenko, V. F., Artemenko, A. G., Shyrykalova, A. O. & Kozhukhar, A. V. (2020). QSPR models for prediction of the melting points and refractive indeces for inorganic substances: components of the optical film-forming materials. Int. J. Quant. Struct. Prop. Relatsh., 5, No. 1, pp. 1-21. https://doi.org/10.4018/IJQSPR.2020010101
Sizochenko, N., Rasulev, B., Gajewicz, V., Puzyn, T. & Leszczynski, J. (2014). From basic physics to mechanisms of toxicity: the “liquid drop” approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. Nanoscale, 6, No. 22, pp. 13986-13993. https://doi.org/10.1039/C4NR03487B
Zinchenko, V. F. (2021). Solid-phase complex compounds and composites of metal oxides, fluorides, and chalcogenides as materials for interference coatings: a review. Theor. Exp. Chem., 57, No. 4, pp. 262-271. https://doi.org/10.1007/s11237-021-09694-2
Lidin, R. А., Аndreeva, L. L. & Molochko, V. А. (1987). Handbook of inorganic chemistry. Constants of inorganic substances. Мoscow: Khimiya (in Russian).
Binnewies, M. & Milke, E. (2002). Thermochemical data of elements and compounds second, revised and extended edition. Weinheim: Wiley-VCH.
Efimov, A. I., Belorukova, L. P., Vasilkova, I. V. & Chechev, V. P. (1983). Properties of inorganic compounds. Handbook. Leningrad: Khimiya (in Russian).
Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard III, W. A. & Skiff, W. M. (1992). UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc., 114, No. 25, pp. 10024-10035. https://doi.org/10.1021/ja00051a040
Rännar, S., Lindgren, F., Geladi, P. & Wold, S. (1994). A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm. J. Chemometrics, 8, No. 2, pp. 111-125. https://doi.org/10.1002/cem.1180080204
Carhart, R. E., Smith, D. H. & Venkataraghavan, R. (1985). Atom pairs as molecular features in structure— activity studies: definition and applications. J. Chem. Inf. Comput. Sci., 25, No. 2, pp. 64-73. https://doi.org/10.1021/ci00046a002
Kuz’min, V. E., Artemenko, A. G., Muratov, E. N., Polischuk, P. G., Ognichenko, L. N., Liahovsky, A. V., Hromov,
A. I. & Varlamova, E. V. (2010). Virtual screening and molecular design based on hierarchical QSAR technology. In Puzyn, T., Leszczynski, J. & Cronin, M. (Eds.). Recent advances in QSAR studies (pp. 127-176). London: Springer. https://doi.org/10.1007/978-1-4020-9783-6_5
Hasegawa, K., Miyashita, Y. & Funatsu, K. (1997). GA strategy for variable selection in QSAR studies: GA- based PLS analysis of calcium channel antagonists. J. Chem. Inf. Comput. Sci., 37, No. 2, pp. 306-310. https://doi.org/10.1021/ci960047x
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