A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

doi:10.1016/S1004-9541(13)60483-8

Abstract

Abstract: Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices (χ), modified molecular connectivity indices (^mχ_t^h) and valance molecular connectivity indices (^mχ^v), with?^mχ^v?calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression (MLR) method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Key words: quantitative structure property relationship, flash point, molecular connectivity indices, hydrogen perturbation, alkane

摘要： Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices (χ), modified molecular connectivity indices (^mχ_t^h) and valance molecular connectivity indices (^mχ^v), with?^mχ^v?calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression (MLR) method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

关键词: quantitative structure property relationship, flash point, molecular connectivity indices, hydrogen perturbation, alkane

Morteza Atabati, Reza Emamalizadeh. A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices[J]. Chin.J.Chem.Eng., 2013, 21(4): 420-426.

Morteza Atabati, Reza Emamalizadeh. A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices[J]. Chinese Journal of Chemical Engineering, 2013, 21(4): 420-426.

References

1 Crowl, D.A., Louvar, J.F., Chemical Process Safety: Fundamentals with Applications, Prentice-Hall, New Jersey (2002).

2 Lyman, W.J., Reehl, W.F., Rosenblatt, D.H., Handbook of Chemical Property Estimation Methods, McGraw-Hill, New York (1982).

3 Suzuki, T., Ohtaguchi, K., Koide, K., “A method for estimating flash points of organic compounds from molecular structures”, Journal of Chemical Engineering of Japan, 24 (3), 258-261 (1991).

4 Tetteh, J., Suzuki, T., Metcalfe, E., Howells, S., “Quantitative structure-property relationships for the estimation of boiling point and flash point using a radial basis function neural network”, Journal of Chemical Information and Computer Sciences, 39 (3), 491-507 (1999).

5 Katritzky, A.R., Petrukhin, R., Jain, R., Karelson, M., “QSPR analysis of flash points”, Journal of Chemical Information and Computer Sciences, 41 (6), 1521-1530 (2001).

6 Albahri, T.A., “Flammability characteristics of pure hydrocarbons”, Chemical Engineering Science, 58 (16), 3629-3641 (2003).

7 Vazhev, V.V., Aldabergenov, M.K., Vazheva, N.V., “Estimation of flash points and molecular masses of alkanes from their IR spectra”, Petroleum Chemistry, 46 (2), 136-139 (2006).

8 Liaw, H.J., Chiu, Y.Y., “The prediction of the flash point for binary aqueous-organic solutions”, Journal of Hazardous Materials, 101 (2), 83-106 (2003).

9 Liaw, H.J., Chiu, Y.Y., “A general model for predicting the flash point of miscible mixtures”, Journal of Hazardous Materials, 137 (1), 38-46 (2006).

10 Pan, Y., Jiang, J., Wang, Z., “Quantitative structure-property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network”, Journal of Hazardous Materials, 147 (1-2), 424-430 (2007).

11 Kier, L.B., Hall, L.H., Molecular Connectivity in Chemistry and Drug Research, Academic Press, New York (1976).

12 Kier, L.B., Hall, L.H., Molecular Connectivity in Structure-Activity Analysis, Research Studies Press, Letch worth, U.K. (1986).

13 Lucic, B., Lukovits, I., Nikolic, S., Trinajstic, N., “Distance-related indexes in the quantitative structure-properties relationship modeling”, Journal of Chemical Information and Computer Sciences, 41 (3), 527-535 (2001).

14 Randic, M., “The connectivity index 25 years after”, Journal of Molecular Graphics and Modelling, 20 (1), 19-35 (2001).

15 Randic, M., Pompe, M., “The variable molecular descriptors based on distance related matrices”, Journal of Chemical Information and Computer Sciences, 41 (3), 575-581 (2001).

16 Pogliani, L. “From molecular connectivity indices to semi empirical connectivity terms: Recent trends in graph theoretical descriptors”, Chemical Reviews, 100 (10), 3827-3858 (2000).

17 Zhong, C., Yang, C., “Approach for the calculation of high-order connectivity indices of polymers and its application”, Journal of Polymer Science Polymer Physics, 40 (5), 401-407 (2002).

18 Devillers, J., Balaban, A.T., Topological Indices and Related Descriptors in QSAR/QSPR, Gordon and Breach, Amsterdam (1999).

19 Bonchev, D., Rouvray, D., Chemical Topology: Applications and Techniques, Taylor & Francis, London (2000).

20 Pogliani, L., “Implementing molecular connectivity theory, a basic tool in modeling drugs”, Journal of Pharmaceutical Sciences, 96 (8), 1856-1871 (2007).

21 Kier, L.B., “Use of molecular negentropy to encode structure governing biological activity”, Journal of Pharmaceutical Sciences, 69 (7), 807-810 (1980).

22 Yang, C., Zhong, C., “Modified connectivity indices and their application to QSPR study”, Journal of Chemical Information and Computer Sciences, 43 (6), 1998-2004 (2003).

23 Pogliani, L., “The hydrogen perturbation in molecular connectivity indices”, Journal of Computational Chemistry, 27 (7), 868-862 (2006).

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