Advanced SearchSearch Tips
QSPR analysis for predicting heat of sublimation of organic compounds
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Analytical Science and Technology
  • Volume 28, Issue 3,  2015, pp.187-195
  • Publisher : The Korean Society of Analytical Science
  • DOI : 10.5806/AST.2015.28.3.187
 Title & Authors
QSPR analysis for predicting heat of sublimation of organic compounds
Park, Yu Sun; Lee, Jong Hyuk; Park, Han Woong; Lee, Sung Kwang;
  PDF(new window)
The heat of sublimation (HOS) is an essential parameter used to resolve environmental problems in the transfer of organic contaminants to the atmosphere and to assess the risk of toxic chemicals. The experimental measurement of the heat of sublimation is time-consuming, expensive, and complicated. In this study, quantitative structural property relationships (QSPR) were used to develop a simple and predictive model for measuring the heat of sublimation of organic compounds. The population-based forward selection method was applied to select an informative subset of descriptors of learning algorithms, such as by using multiple linear regression (MLR) and the support vector machine (SVM) method. Each individual model and consensus model was evaluated by internal validation using the bootstrap method and y-randomization. The predictions of the performance of the external test set were improved by considering their applicability to the domain. Based on the results of the MLR model, we showed that the heat of sublimation was related to dispersion, H-bond, electrostatic forces, and the dipole-dipole interaction between inter-molecules.
Heat of sublimation;QSPR;MLR;SVM;consensus model;
 Cited by
다양한 유기화합물의 비등점 예측을 위한 QSPR 모델 및 이의 적용구역,신성은;차지영;김광연;노경태;

분석과학, 2015. vol.28. 4, pp.270-277 crossref(new window)
QSPR model for the boiling point of diverse organic compounds with applicability domain, Analytical Science and Technology, 2015, 28, 4, 270  crossref(new windwow)
K. Nakajoh, E. Shibata, T. Todoroki, A. Ohara, K. Nishizawa and T. Nakamura, Environ. Toxicol. Chem., 25(2), 327-336 (2006). crossref(new window)

P. Politzer, Y. Ma, P. Lane and M. C. Concha, Int. J. Quantum Chem, 105(4), 341-347 (2005). crossref(new window)

F. Gharagheizi, P. Ilani-Kashkouli, W. E. Acree, A. H. Mohammadi and D. Ramjugernath, Fluid Phase Equilib., 354, 265-285 (2013). crossref(new window)

F. Gharagheizi, Thermochim. Acta, 469(1-2), 8-11 (2008). crossref(new window)

E. H. Jean, J. H. J. Park, Jin Hee and S. K. Lee, Anal. Sci. Technol., 24(6), 533-543 (2011). crossref(new window)

I. S. Song, J. Y. Cha and S. K. Lee, Anal. Sci. Technol., 24(6), 544-555 (2011). crossref(new window)

W. Acree and J. S. Chickos, J. Phys. Chem. Ref. Data, 39(4), 043101 (2010). crossref(new window)

M. A. V. Roux, M. Temprado, J. S. Chickos and Y. Nagano, J. Phys. Chem. Ref. Data, 37(4), 1855-1996 (2008). crossref(new window)

PreADMET Ver., BMDRC: Seoul Korea, 2007.

G. Schneider, W. Neidhart, T. Giller and G. Schmid, Angew. Chem. Int. Ed. Engl., 38(19), 2894-2896 (1999). crossref(new window)

RapidMiner Ver.5.3.012, Rapid-I: Stockumer, Germany.

C. Cortes and V. Vapnik, Mach. Learn., 20(3), 273-297 (1995).

B. Schölkopf, A. J. Smola, R. C. Williamson,and P. L. Bartlett, Neural Comput., 12(5), 1207-1245 (2000). crossref(new window)

A. Tropsha, P. Gramatica and V. K. Gombar, QSAR Combi. Sci., 22(1), 69-77 (2003). crossref(new window)