Quantitative modeling for prediction of boiling points of phenolic compounds

Year 2019, Volume: 3 Issue: 2, 121 - 128, 31.12.2019

Soumaya Kherouf Nabil Bouarra , Djelloul Messadi

https://doi.org/10.32571/ijct.636581

Cited By: 1

Abstract

This work aims to reveal the correlation of the boiling point values of
phenolic compounds with their molecular structures using a quantitative
structure-property relationship (QSPR) approach. A large number of molecular
descriptors have been calculated from molecular structures by the DRAGON software.
In this study, all 56 phenolic compounds were divided into two subsets: one for
the model formation and the other for external validation, by using the Kennard
and Stone algorithm. A four-descriptor model was constructed by applying a
multiple linear regression based on the ordinary least squares regression
method and genetic algorithm/variables subsets selection. The good of fit and
predictive power of the proposed model were evaluated by different approaches,
including single or multiple output cross-validations, the Y-scrambling test,
and external validation through prediction set. Also,
the applicability domain of the developed model was examined using Williams
plot. The model shows R² = 0.876, Q²_LOO = 0.841, Q²_LMO =
0.831 and Q²_EXT = 0.848. The results obtained demonstrate that the
model is reliable with good predictive accuracy.

Keywords

Phenolic compounds, Boiling point, QSPR, MLR, Prediction set

References

• 1. Michałowicz, J.; Duda, R.O.W. Water. Air. Soil. Pollut. 2005,16, 205-222.
• 2. Todeschini, R.; Gramatica, P.; Provenazi, R.; Marengo, E.; Chemom. Intell. Lab. Syst. 1995, 27, 221-229.
• 3. Gharagheizi, F.; Mirkhani, S. A.; Ilani-Kashkouli, P.; Mohammadi, A. H.; Ramjugernath, D. Richon, D.; Fluid Phase Equilib. 2013, 354, 250-258.
• 4. Katritzky, A. R.; Mu, L.; Lobanov, V. S. J. Phys. Chem. 1996, 100, 10400-10407.
• 5. Yi-min, D.; Zhi-ping, Z.; Zhong, C.; Yue-fei, Z.; Ju-lan, Z.; Xun L. J. Mol. Graphics. Modell. 2013, 44, 113-119.
• 6. White, C.M. J. Chem. Eng. Data. 1986, 31, 198-203.
• 7. Smeeks, F.C.; Jurs, P.C. Anal. Chim. Acta. 1990, 233, 111-119.
• 8. Admire, B.; Lian, B.; Yalkowsky, S. H. Chemosphere. 2015, 119, 1436–1440.
• 9. Shuai, D. ;Wen, S.; Li, Zhao. Int. J. Refrig. 2016, 63, 63–71.
• 10. Liangjie, J.; Peng B. ‎Chemom. Intell. Lab. Syst. 2016, 157, 127–132.
• 11. Ramane, H. S.;• Yalnaik, A. S. J. Appl. Math. Comput. 2017, 55, 1–2, 609–627.
• 12. Varamesh, A.; Hemmati-Sarapardeh, A.; Dabir, B.; Mohammadi, A.H. J. Mol. Liq, 2017, 242, 59-69.
• 13. Arjmand, F.; Shafiei, F. J. Struc. Chem. 2018, 59, 3, 748-754.
• 14. Katritzky,A. R.; Maran, U.; Lobanov,V. S.; Karelson, M. J. Chem. Inf. Comput. Sci. 2000, 40, 1-18.
• 15. Katritzky, A. R.; Lobanov,V. S.; Karelson, M. Chem. Soc. Rev. 1995, 24, 279-287.
• 16. Mackay, D.; Shiu,W.Y.; Ma, K.C.; Lee, S.C. Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742.Vol III, 2006.
• 17. Kennard, R.; Stone, L.A. Technometrics. 1969, 11, 137-148.
• 18. HyperChem 6.03 Package. Hypercube, Inc., Gainesville, Florida, USA, 1999, software available at: http://www.hyper.com.
• 19. Gaber, M.M. Scientific Data Mining and Knowledge Discovery: Principles and Foundations; Springer Heidelberg Dordrecht London, Berlin, 2009.
• 20. Talete Srl. Dragon for Windows (Software for Molecular Descriptor Calculation) Version 5.5 Milano, Italy, 2007, software available at: http://www.talete.mi.it.
• 21. Leardi, R.; Boggia, R,; Terrile, M. J. Chemom. 1992, 6, 267-281.
• 22. Todeschni, R.; Ballabio, D.; Consonni, V.; Mauri, A.; Pavan, M. 2009. Mobydigs – version 1.1 – Copyright TALETE Srl.
• 23. Todeschini R.; Maiocchi A.; Consonni, V. Chemom. Int. Lab. Syst. 1999, 46, 13-29.
• 24. Tropsha, A.; Gramatica, P.; Gombar, V. K. QSAR Comb. Sci. 2003, 22, 70-77.
• 25. Golbraikh, A.; Tropsha, A. J. Mol. Graph. Model. 2002, 20, 269-276.
• 26. Yu, X. L.; Yi, B.; Yu,W. H.; Wang, X. Y. Chem. Pap. 2008, 62, 623-229.
• 27. Netzeva, T.I.; Worth, A.P.; Aldenberg, T.; Benigni, R.; Cronin, M.T.D.; Gramatica, P.; Jaworska, J.S.; Kahn, S.; Klopman, G.; Marchant, C.A.; Myatt, G.; Nikolova-Jeliazkova, N.; Patlewicz, G.Y.; Perkins, R.; Roberts, D.W.; Schultz, T.W.; Stanton, D.T.; vande Sandt, J.J.M.; Tong, W.;Veith, G.; Yang, C. Altern. Lab. Anim. 2005, 33, 155–173.
• 28. Gramatica, P.; Cassani, S. Roy, P. P. Kovarich, S.; Yap. C. W. Papa, E. Mol. Inform. 2012, 31, 817-835.
• 29. Eriksson, L.; Jaworska, J.; Worth, A.P.; Cronin, M.T.D.; McDowell, R.M.; Gramatica, P. Environ. Health. Perspect. 2003, 111, 1361-1375.
• 30. Jaiswal, M.; Khadikar, P.V.; Scozzafava,A.; Supuran, C.T. Bioorg. Med. Chem. Lett. 2004, 14, 3283-3290.
• 31. Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics; Wiley-VCH: Weinheim, Germany, 2009.
• 32. Gramatica, P.; Navas, N.; Todeschini, R. Trends Anal. Chem. 1999b, 18, 461-471.
• 33. Randic, M.; Razinger, M. J. Chem. Inf. Model. 1995, 35, 140-147.
• 34. Mitra I.; Saha A.; Roy K. Mol. Simul. 2010, 36, 1067-1079.
• 35. Ray, S.; Sengupta, C.; Roy K. Cent. Eur. J. Chem. 2007, 5, 1094-1113.
• 36. Randic, M. J. Chem. Inf. Model. 2001, 41, 607-613.
• 37. Abbasi, M.; Sadeghi-Aliabadi, H.; Amanlou, M. J. Pharm. Sci. 2017, 25, 1-17.
• 38. Consonni,V.; Todeschini, R.; Pavan, M. J. Chem. Inf. Comput. Sci. 2002, 42, 682-692.
• 39. Consonni,V.; Todeschini, R.; Pavan, M.; Gramatica, P. J. Chem. Inf. Comput. Sci. 2002, 42, 693-705.