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Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks

Received: 03 May 2016    Accepted:     Published: 04 May 2016
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Abstract

Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35(64)-1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0.994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.

DOI 10.11648/j.mc.20160402.12
Published in Modern Chemistry (Volume 4, Issue 2, April 2016)
Page(s) 24-29
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Radial Basis Networks, Normal Boiling Point, Hydroxyl Compounds, QSPR Model

References
[1] S. Gamba, G.S. Soave, L.A. Pellegrini, “Use of normal boiling point correlations for predicting critical parametersof paraffins for vapour–liquid equilibrium calculations with the SRK equation of state,” Fluid Phase Equil., 2009, vol. 276, pp. 133–141.
[2] E. Panteli, E. Voutsas, K. Magoulas, et al., “Prediction of vapor pressures and enthalpies of vaporization of organic compounds from the normal boiling point temperature,” Fluid Phase Equil., 2006, vol. 248, pp.70-77.
[3] J. Marrero, and R. Gani, “Group-contribution based estimation of pure component properties,” Fluid Phase Equil., 2001, vol. 183/184, pp. 183-208.
[4] J. Guilherme, J. Maximo, J.A. Antonio, et al, “Boiling point of aqueous d-glucose and d-fructose solutions: Experimental determination and modeling with group-contribution method,” Fluid Phase Equil., 2010, vol. 299, pp. 32-41.
[5] D. Sola, A. Ferri, M. Banchero, et al, “QSPR prediction of N-boiling point and critical properties of organic compounds and comparison with a group-contribution method,” Fluid Phase Equil., 2008, vol. 263, pp. 33–42.
[6] I. Oprisiu, G. Marcou, D. Horvath, S., et al, “Publicly available models to predict normal boiling point of organic compounds,” Thermochim. Acta, 2013, vol. 553, pp. 60-67.
[7] Y.M. Dai, Z.P. Zhu, Z. Cao, et. al, “Prediction of boiling points of organic compounds by QSPR tools,” J. Mol. Graph. Model., 2013, vol. 44, pp. 113-119.
[8] D. Abooali, M.A. Sobati, “Novel method for prediction of normal boiling point and enthalpy of vaporization at normal boiling point of pure refrigerants: A QSPR approach,” Int. J. Refrig., 2014, vol. 40, pp. 282-293.
[9] V. Zare-Shahabadi, M. Lotfizadeh, A.R.A. Gandomani, “Determination of boiling points of azeotropic mixtures using quantitative structure–property relationship (QSPR) strategy,” J. Mol. Liq., 2013, vol. 188, pp. 222-229
[10] Q.F, Li, X.G. Chen, Z.D. Hu, “Quantitative structure-property relationship studies for estimating boiling points of alcohols using calculated molecular descriptors with radial basis function neural networks,” Chemometr. Intell. Lab., 2004, vol. 72, pp. 93-100.
[11] F. Gharagheizi, S.A. Mirkhani, P. Ilani-Kashkouli, et al, “Determination of the normal boiling point of chemical compounds using a quantitative structure-property relationship strategy: Application to a very large dataset.” Fluid Phase Equil., 2013, vol. 354, pp. 250-258.
[12] G.Q. Liu, L.X. Ma, S.G. Xiang, “Handbook of chemistry and chemical properties data”, 4rd ed, Beijing: Chemical Industry Press, 2013.
[13] F. Gharagheizi, and M. Sattari. “Prediction of triple point temperature of pure components using their chemical structure,” Ind. Eng. Chem. Res., 2010, vol. 49, pp. 929-932.
[14] VCCLAB, http://www.vcclab.org, 2005
[15] I.V. Tetko, J. Gasteiger, J., R. Todeschini, R. et al. “Virtual computational chemistry laboratory - design and description,” J. Comput. Aid. Mol. Des., 2005, vol. 19, pp. 453-63.
[16] R. Todeschini, and V. Consonni, “Molecular descriptors for chemoinformatics,” 2rd ed. Weinheim: Wiley-VCH, 2009.
[17] N.S. Sapre, N. Pancholi, S. Gupta, “Computational modeling of substitution effect on HIV–1non-nucleoside reverse transcriptase inhibitors with Kier–Hall electrotopological state (E–state) indice,” Int. Electron. J. Mol. Des., 2008, vol. 7, pp. 55–67.
[18] V. Consonni, R. Todeschini,M. Pavan, “Structure/Response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors,” J. Chem. Inf. Comput. Sci., 2002, vol. 42, pp. 682-692.
[19] V. Consonni, R. Todeschini, M. Pavan, et al, “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, vol. 42, pp. 693-705.
[20] R. Todeschini, P. Gramatica, E. Marengor, et al, “Weighted holistic invariant molecular descriptors. Part 2. Theory development and applications on modeling physicochemical properties of polyaromatic hydrocarbons.” Intell. Lab. Syst, 1995, vol. 27, pp. 221-229.
[21] O. Devinyak, D. Havrylyuk, R. Lesyk, “3D-MoRSE descriptors explained,” J. Mol. Graph. Model., 2014, vol. 54, pp. 194–203.
Author Information
  • School of Chemical Engineering and Technology, Tianjin University, Tianjin, PR China

  • School of Chemical Engineering and Technology, Tianjin University, Tianjin, PR China

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    Liangjie Jin, Peng Bai. (2016). Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks. Modern Chemistry, 4(2), 24-29. https://doi.org/10.11648/j.mc.20160402.12

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    Liangjie Jin; Peng Bai. Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks. Mod. Chem. 2016, 4(2), 24-29. doi: 10.11648/j.mc.20160402.12

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    AMA Style

    Liangjie Jin, Peng Bai. Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks. Mod Chem. 2016;4(2):24-29. doi: 10.11648/j.mc.20160402.12

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  • @article{10.11648/j.mc.20160402.12,
      author = {Liangjie Jin and Peng Bai},
      title = {Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks},
      journal = {Modern Chemistry},
      volume = {4},
      number = {2},
      pages = {24-29},
      doi = {10.11648/j.mc.20160402.12},
      url = {https://doi.org/10.11648/j.mc.20160402.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.mc.20160402.12},
      abstract = {Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35(64)-1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0.994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks
    AU  - Liangjie Jin
    AU  - Peng Bai
    Y1  - 2016/05/04
    PY  - 2016
    N1  - https://doi.org/10.11648/j.mc.20160402.12
    DO  - 10.11648/j.mc.20160402.12
    T2  - Modern Chemistry
    JF  - Modern Chemistry
    JO  - Modern Chemistry
    SP  - 24
    EP  - 29
    PB  - Science Publishing Group
    SN  - 2329-180X
    UR  - https://doi.org/10.11648/j.mc.20160402.12
    AB  - Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35(64)-1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0.994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.
    VL  - 4
    IS  - 2
    ER  - 

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