Prediction the Normal Boiling Points of Primary, Secondary and Tertiary Liquid Amines from their Molecular Structure Descriptors
Saaidpour Saadi 1*, Bahmani Asrin 2, Rostami Amin 2
1Department of Chemistry, Faculty of Science
Sanandaj Branch, Islamic Azad University, Sanandaj, Iran2Department of Chemistry, Faculty of Science
Kurdistan University, Sanandaj, Iran∗E-mail: sasaaidpour@iausdj.ac.ir
Received:
Received: 23 September 2015; revised: 19 November 2015; accepted: 28 November 2015; published online: 09 December 2015
DOI: 10.12921/cmst.2015.21.04.004
Abstract:
In this article, at first, a quantitative structure–property relationship (QSPR) model for estimation of the normal boiling point of liquid amines is developed. QSPR study based multiple linear regression was applied to predict the boiling points of primary, secondary and tertiary amines. The geometry of all amines was optimized by the semiempirical method AM1 and used to calculate different types of molecular descriptors. The molecular descriptors of structures were calculated using Molecular Modeling Pro plus software. Stepwise regression was used for selection of relevance descriptors. The linear models developed with Molegro Data Modeller (MDM) allow accurate estimate of the boiling points of amines using molar mass (MM), Hansen dispersion forces (DF), molar refractivity (MR) and hydrogen bonding (HB) (1◦ and 2◦ amines) descriptors. The information encoded in the descriptors allows an interpretation of the boiling point studied based on the intermolecular interactions. Multiple linear regression (MLR) was used to develop three linear models for 1◦ , 2◦ and 3◦ amines containing four and three variables with a high precision root mean squares error, 15.92 K, 9.89 K and 15.76 K
and a good correlation with the squared correlation coefficient 0.96, 0.98 and 0.96, respectively. The predictive power and robustness of the QSPR models were characterized by the statistical validation and applicability domain (AD).
Key words:
References:
[1] J.E. McMurry, Organic Chemistry (3rd ed.), Belmont: Wadsworth 1992.
[2] L.B. Kier and L.H. Hall, Molecular Connectivity in Chemistry and Drug Research, Academic Press, NewYork 1976.
[3] W.J. Lyman, W.F. Reehl and D.H. Rosenblatt, Handbook of Chemical Property Estimation Methods, American Chemical Society,
Washington DC 1990.
[4] C.H. Fisher, Boiling Point Gives Critical Temperatures, Chem. Eng., 96, 157-158 (1989).
[5] K. Satyanarayana and M.C. Kakati, Note: Correlation of flash points, Fire Mater., 15, 97-100 (1991).
[6] C.E. Rechsteiner, In handbook of chemical property estimation methods, McGraw-Hill: NewYork 1982.
[7] J. Ghasemi and S. Saaidpour, Artificial neural network-based quantitative structural property relationship for predicting boiling
points of refrigerants, QSAR & Comb. Sci., 28, 1245-1254 (2009).
[8] L.M. Egolf and P.C. Jurs, Prediction of boiling points of organic heterocyclic compounds using regression and neural network
techniques, J. Chem. Inf. Comp. Sci., 33, 616-625 (1993).
[9] L.H. Hall and L.B. Kier, Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and
Valence State Information, J. Chem. Inf. Comput. Sci., 35, 1039-1045 (1995).
[10] O. Ivanciuc, T. Ivanciuc and A.T. Balaban, Quantitative structure-property relationship study of normal boiling points for halogen-/
oxygen-/ sulfur-containing organic compounds using the CODESSA program, Tetrahedron, 54, 9129-9142 (1998).
[11] D. Plavsic, N. Trinajstic, D. Amic, et al., Comparison between the structure–boiling point relationships with different descriptors for
condensed benzenoids, New J. Chem., 22, 1075-1078 (1998).
[12] O. Ivanciuc, T. Ivanciuc and A.T. Balaban, Design of topological indices. Part 10. Parameters based on electronegativity and covalent
radius for the computation of molecular graph descriptors for heteroatom-containing molecules, J. Chem. Inf. Comput. Sci., 38,
395-401 (1998).
[13] A.P. Bunz, B. Braun and R. Janowsky, Application of quantitative structure-performance relationship and neural network models for
the prediction of physical properties from molecular structure, Ind. Eng. Chem. Res., 37, 3043-3051 (1998).
[14] D.T. Stanton and P.C. Jurs, Development and use of charged partial surface area structural descriptors in computer-assisted
quantitative structure-property relationship studies, Anal. Chem., 62, 2323-2329 (1990).
[15] M.D. Wessel and P.C. Jurs, Prediction of normal boiling points of hydrocarbons from molecular structure, J. Chem. Inf. Comput.
Sci., 35, 68-76 (1995).
[16] L.H. Hall and C.T. Story, Boiling point and critical temperature of a heterogeneous data set: QSAR with atom type electrotopological
state indices using artificial neural networks, J. Chem. Inf. Comput. Sci., 36,1004-1014 (1996).
[17] E.S. Goll and P.C. Jurs, Prediction of the normal boiling points of organic compounds from molecular structures with a computational
neural network model, J. Chem. Inf. Comput. Sci., 39, 974-983 (1999).
[18] A.R. Katritzky, M. Kuanar, S. Slavov, et al., Quantitative Correlation of Physical and Chemical Properties with Chemical Structure:
Utility for Prediction, Chem. Rev., 110, 5714-5789 (2010).
[19] V. Sharma , R. Goswami and A.K. Madan, A novel highly discriminating topological descriptor for structure–property and
structure–activity studies, J. Chem. Inf. Comput. Sci., 37, 273-282 (1997).
[20] L.B. Kier and L.H. Hall, Molecular Structure Description-The Electrotopological State, Academic, San Diego, CA, USA 1999.
[21] J. Ghasemi and S. Saaidpour, quantitative structure–property relationship study of n-octanol–water partition coefficients of some of
diverse drugs using multiple linear regression, Anal. Chim. Acta, 604, 99-106 (2007).
[22] S. Saaidpour, Prediction of drug lipophilicity using back propagation artificial neural network modeling, Orient. J. Chem., 30(2),
793-802 (2014).
[23] Y. Pan, J. Jiang, R. Wang, et al., A novel QSPR model for prediction of lower flammability limits of organic compounds based on
support vector machine, J. Hazard. Mater., 168, 962-969 (2009).
[24] R.L. Shriner, C.K.F. Hermann, T.C. Morrill, et al., The Systematic Identification of Organic Compounds, eights edition, John Wiley
& Sons. Inc. 2004.
[25] L.B. Kier and L.H. Hall, Molecular Connectivity in Structure-Activity Analysis, RSP-Wiley, Chichetser, UK 1986.
[26] R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Weinheim, Germany 2000.
[27] R.R. Hocking, The Analysis and Selection of Variables in Linear Regression, Biometrics 1976.
[28] S. Saaidpour, Prediction of the Adsorption Capability onto Activated Carbon of Liquid Aliphatic Alcohols using Molecular Fragments
Method, Iran. J. Math. Chem., 5 (2), 127-142 (2014).
[29] A.C. Atkinson, In Plots, Transformations and Regression, Clarendon Press, Oxford 1985.
[30] P. Gemperline, Practical Guide to Chemometrics, Taylor & Francis Group, Boca Raton 2006.
[31] A. Golbraikh and A. Tropsha, Beware of q2 !, J. Mol. Graph. Model., 20, 269-276 (2002).
[32] P. Gramatica, P. Pilutti and E. Papa, ValidatedQSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting intoTraining–Test
Sets and Consensus Modeling, J. Chem. Inf. Comput. Sci., 44, 1794-1802 (2004).
[33] P. Gramatica, Principles of QSAR models validation: internal and external, QSAR & Comb. Sci., 26, 694-701 (2007).
[34] P. Gramatica, E. Giani and E. Papa, Statistical external validation and consensus modeling: A QSPR case study for Koc prediction, J.
Mol. Graph. Model., 25, 755-766 (2007).
[35] A. Tropsha, P. Gramatica and V.K. Gombar, The Importance of Being Earnest: Validation is the Absolute Essential for Successful
Application and Interpretation of QSPR Models, QSAR & Comb. Sci., 22, 69-77 (2003).
[36] L. Eriksson, J. Jaworska, A.P. Worth, et al., Methods for reliability and uncertainty assessment and for applicability evaluations of
classification- and regression-based QSARs, Environ. Health Perspect. Aug., 111, 1351-1375 (2003).
[37] S. Weaver and M.P. Gleeson, The importance of the domain of applicability in QSAR modeling, J. Mol. Graph. Model., 26, 1315-1326
(2008).
[38] A.C. Atkinson, Plots, Transformations and Regression, Clarendon Press, Oxford 1985.
[39] M. Shacham, N. Brauner, G.S. Cholakov, et al., Identifying Applicability Domains for Quantitative Structure Property Relationships,
Elsevier, Amsterdam 2009.
[40] F. Sahigara, K. Mansouri, D. Ballabio, et al., Comparison of different approaches to define the applicability domain of qsar models,
Molecules, 17, 4791-4810 (2012).
[41] Ch. M. Hansen, Hansen Solubility Parameters, A User’s Handbook, Second Edition, CRC Press, 2007.
[42] R. Todeschini, V. Consonni, Molecular Descriptors for Chemoinformatics, Vol. I & II, WILEY-VCH, 2009.
In this article, at first, a quantitative structure–property relationship (QSPR) model for estimation of the normal boiling point of liquid amines is developed. QSPR study based multiple linear regression was applied to predict the boiling points of primary, secondary and tertiary amines. The geometry of all amines was optimized by the semiempirical method AM1 and used to calculate different types of molecular descriptors. The molecular descriptors of structures were calculated using Molecular Modeling Pro plus software. Stepwise regression was used for selection of relevance descriptors. The linear models developed with Molegro Data Modeller (MDM) allow accurate estimate of the boiling points of amines using molar mass (MM), Hansen dispersion forces (DF), molar refractivity (MR) and hydrogen bonding (HB) (1◦ and 2◦ amines) descriptors. The information encoded in the descriptors allows an interpretation of the boiling point studied based on the intermolecular interactions. Multiple linear regression (MLR) was used to develop three linear models for 1◦ , 2◦ and 3◦ amines containing four and three variables with a high precision root mean squares error, 15.92 K, 9.89 K and 15.76 K
and a good correlation with the squared correlation coefficient 0.96, 0.98 and 0.96, respectively. The predictive power and robustness of the QSPR models were characterized by the statistical validation and applicability domain (AD).
Key words:
References:
[1] J.E. McMurry, Organic Chemistry (3rd ed.), Belmont: Wadsworth 1992.
[2] L.B. Kier and L.H. Hall, Molecular Connectivity in Chemistry and Drug Research, Academic Press, NewYork 1976.
[3] W.J. Lyman, W.F. Reehl and D.H. Rosenblatt, Handbook of Chemical Property Estimation Methods, American Chemical Society,
Washington DC 1990.
[4] C.H. Fisher, Boiling Point Gives Critical Temperatures, Chem. Eng., 96, 157-158 (1989).
[5] K. Satyanarayana and M.C. Kakati, Note: Correlation of flash points, Fire Mater., 15, 97-100 (1991).
[6] C.E. Rechsteiner, In handbook of chemical property estimation methods, McGraw-Hill: NewYork 1982.
[7] J. Ghasemi and S. Saaidpour, Artificial neural network-based quantitative structural property relationship for predicting boiling
points of refrigerants, QSAR & Comb. Sci., 28, 1245-1254 (2009).
[8] L.M. Egolf and P.C. Jurs, Prediction of boiling points of organic heterocyclic compounds using regression and neural network
techniques, J. Chem. Inf. Comp. Sci., 33, 616-625 (1993).
[9] L.H. Hall and L.B. Kier, Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and
Valence State Information, J. Chem. Inf. Comput. Sci., 35, 1039-1045 (1995).
[10] O. Ivanciuc, T. Ivanciuc and A.T. Balaban, Quantitative structure-property relationship study of normal boiling points for halogen-/
oxygen-/ sulfur-containing organic compounds using the CODESSA program, Tetrahedron, 54, 9129-9142 (1998).
[11] D. Plavsic, N. Trinajstic, D. Amic, et al., Comparison between the structure–boiling point relationships with different descriptors for
condensed benzenoids, New J. Chem., 22, 1075-1078 (1998).
[12] O. Ivanciuc, T. Ivanciuc and A.T. Balaban, Design of topological indices. Part 10. Parameters based on electronegativity and covalent
radius for the computation of molecular graph descriptors for heteroatom-containing molecules, J. Chem. Inf. Comput. Sci., 38,
395-401 (1998).
[13] A.P. Bunz, B. Braun and R. Janowsky, Application of quantitative structure-performance relationship and neural network models for
the prediction of physical properties from molecular structure, Ind. Eng. Chem. Res., 37, 3043-3051 (1998).
[14] D.T. Stanton and P.C. Jurs, Development and use of charged partial surface area structural descriptors in computer-assisted
quantitative structure-property relationship studies, Anal. Chem., 62, 2323-2329 (1990).
[15] M.D. Wessel and P.C. Jurs, Prediction of normal boiling points of hydrocarbons from molecular structure, J. Chem. Inf. Comput.
Sci., 35, 68-76 (1995).
[16] L.H. Hall and C.T. Story, Boiling point and critical temperature of a heterogeneous data set: QSAR with atom type electrotopological
state indices using artificial neural networks, J. Chem. Inf. Comput. Sci., 36,1004-1014 (1996).
[17] E.S. Goll and P.C. Jurs, Prediction of the normal boiling points of organic compounds from molecular structures with a computational
neural network model, J. Chem. Inf. Comput. Sci., 39, 974-983 (1999).
[18] A.R. Katritzky, M. Kuanar, S. Slavov, et al., Quantitative Correlation of Physical and Chemical Properties with Chemical Structure:
Utility for Prediction, Chem. Rev., 110, 5714-5789 (2010).
[19] V. Sharma , R. Goswami and A.K. Madan, A novel highly discriminating topological descriptor for structure–property and
structure–activity studies, J. Chem. Inf. Comput. Sci., 37, 273-282 (1997).
[20] L.B. Kier and L.H. Hall, Molecular Structure Description-The Electrotopological State, Academic, San Diego, CA, USA 1999.
[21] J. Ghasemi and S. Saaidpour, quantitative structure–property relationship study of n-octanol–water partition coefficients of some of
diverse drugs using multiple linear regression, Anal. Chim. Acta, 604, 99-106 (2007).
[22] S. Saaidpour, Prediction of drug lipophilicity using back propagation artificial neural network modeling, Orient. J. Chem., 30(2),
793-802 (2014).
[23] Y. Pan, J. Jiang, R. Wang, et al., A novel QSPR model for prediction of lower flammability limits of organic compounds based on
support vector machine, J. Hazard. Mater., 168, 962-969 (2009).
[24] R.L. Shriner, C.K.F. Hermann, T.C. Morrill, et al., The Systematic Identification of Organic Compounds, eights edition, John Wiley
& Sons. Inc. 2004.
[25] L.B. Kier and L.H. Hall, Molecular Connectivity in Structure-Activity Analysis, RSP-Wiley, Chichetser, UK 1986.
[26] R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Weinheim, Germany 2000.
[27] R.R. Hocking, The Analysis and Selection of Variables in Linear Regression, Biometrics 1976.
[28] S. Saaidpour, Prediction of the Adsorption Capability onto Activated Carbon of Liquid Aliphatic Alcohols using Molecular Fragments
Method, Iran. J. Math. Chem., 5 (2), 127-142 (2014).
[29] A.C. Atkinson, In Plots, Transformations and Regression, Clarendon Press, Oxford 1985.
[30] P. Gemperline, Practical Guide to Chemometrics, Taylor & Francis Group, Boca Raton 2006.
[31] A. Golbraikh and A. Tropsha, Beware of q2 !, J. Mol. Graph. Model., 20, 269-276 (2002).
[32] P. Gramatica, P. Pilutti and E. Papa, ValidatedQSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting intoTraining–Test
Sets and Consensus Modeling, J. Chem. Inf. Comput. Sci., 44, 1794-1802 (2004).
[33] P. Gramatica, Principles of QSAR models validation: internal and external, QSAR & Comb. Sci., 26, 694-701 (2007).
[34] P. Gramatica, E. Giani and E. Papa, Statistical external validation and consensus modeling: A QSPR case study for Koc prediction, J.
Mol. Graph. Model., 25, 755-766 (2007).
[35] A. Tropsha, P. Gramatica and V.K. Gombar, The Importance of Being Earnest: Validation is the Absolute Essential for Successful
Application and Interpretation of QSPR Models, QSAR & Comb. Sci., 22, 69-77 (2003).
[36] L. Eriksson, J. Jaworska, A.P. Worth, et al., Methods for reliability and uncertainty assessment and for applicability evaluations of
classification- and regression-based QSARs, Environ. Health Perspect. Aug., 111, 1351-1375 (2003).
[37] S. Weaver and M.P. Gleeson, The importance of the domain of applicability in QSAR modeling, J. Mol. Graph. Model., 26, 1315-1326
(2008).
[38] A.C. Atkinson, Plots, Transformations and Regression, Clarendon Press, Oxford 1985.
[39] M. Shacham, N. Brauner, G.S. Cholakov, et al., Identifying Applicability Domains for Quantitative Structure Property Relationships,
Elsevier, Amsterdam 2009.
[40] F. Sahigara, K. Mansouri, D. Ballabio, et al., Comparison of different approaches to define the applicability domain of qsar models,
Molecules, 17, 4791-4810 (2012).
[41] Ch. M. Hansen, Hansen Solubility Parameters, A User’s Handbook, Second Edition, CRC Press, 2007.
[42] R. Todeschini, V. Consonni, Molecular Descriptors for Chemoinformatics, Vol. I & II, WILEY-VCH, 2009.
