Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 3 Sayı: 2, 121 - 128, 31.12.2019
https://doi.org/10.32571/ijct.636581

Öz

Kaynakça

  • 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.

Quantitative modeling for prediction of boiling points of phenolic compounds

Yıl 2019, Cilt: 3 Sayı: 2, 121 - 128, 31.12.2019
https://doi.org/10.32571/ijct.636581

Öz

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, Q²LOO = 0.841, Q²LMO =
0.831 and Q²EXT = 0.848. The results obtained demonstrate that the
model is reliable with good predictive accuracy.

Kaynakça

  • 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.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kimya Mühendisliği
Bölüm Makale
Yazarlar

Soumaya Kherouf Bu kişi benim 0000-0001-9797-3746

Nabil Bouarra 0000-0001-5438-8678

Djelloul Messadi Bu kişi benim 0000-0003-3257-9590

Yayımlanma Tarihi 31 Aralık 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 3 Sayı: 2

Kaynak Göster

APA Kherouf, S., Bouarra, N., & Messadi, D. (2019). Quantitative modeling for prediction of boiling points of phenolic compounds. International Journal of Chemistry and Technology, 3(2), 121-128. https://doi.org/10.32571/ijct.636581
AMA Kherouf S, Bouarra N, Messadi D. Quantitative modeling for prediction of boiling points of phenolic compounds. Int. J. Chem. Technol. Aralık 2019;3(2):121-128. doi:10.32571/ijct.636581
Chicago Kherouf, Soumaya, Nabil Bouarra, ve Djelloul Messadi. “Quantitative Modeling for Prediction of Boiling Points of Phenolic Compounds”. International Journal of Chemistry and Technology 3, sy. 2 (Aralık 2019): 121-28. https://doi.org/10.32571/ijct.636581.
EndNote Kherouf S, Bouarra N, Messadi D (01 Aralık 2019) Quantitative modeling for prediction of boiling points of phenolic compounds. International Journal of Chemistry and Technology 3 2 121–128.
IEEE S. Kherouf, N. Bouarra, ve D. Messadi, “Quantitative modeling for prediction of boiling points of phenolic compounds”, Int. J. Chem. Technol., c. 3, sy. 2, ss. 121–128, 2019, doi: 10.32571/ijct.636581.
ISNAD Kherouf, Soumaya vd. “Quantitative Modeling for Prediction of Boiling Points of Phenolic Compounds”. International Journal of Chemistry and Technology 3/2 (Aralık 2019), 121-128. https://doi.org/10.32571/ijct.636581.
JAMA Kherouf S, Bouarra N, Messadi D. Quantitative modeling for prediction of boiling points of phenolic compounds. Int. J. Chem. Technol. 2019;3:121–128.
MLA Kherouf, Soumaya vd. “Quantitative Modeling for Prediction of Boiling Points of Phenolic Compounds”. International Journal of Chemistry and Technology, c. 3, sy. 2, 2019, ss. 121-8, doi:10.32571/ijct.636581.
Vancouver Kherouf S, Bouarra N, Messadi D. Quantitative modeling for prediction of boiling points of phenolic compounds. Int. J. Chem. Technol. 2019;3(2):121-8.