Skip to main content
Log in

QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method

  • Original Research
  • Published:
Structural Chemistry Aims and scope Submit manuscript

Abstract

Quantitative structure–property relationship (QSPR) models are built for the set of 233 very different organic chemical compounds for the estimation of the solute polarity parameter p. QSPR models for the solute polarity parameter p are calculated with optimal descriptors based on a SMILES notation, and all models were developed using the Monte Carlo method where the end point is threaded as a random event. Models were prepared in accordance with the OECD principles and recommendations. Three random splits into the training, test, and validation sets were examined. The statistical quality of all build models was very good. The best calculated model had the following statistical parameters: for the training set r 2 = 0.9493, q 2 = 0.9479, s = 0.285, F = 2564; r 2 = 0.9608, q 2 = 0.9561, s = 0.249, F = 1102 for the test set; and r 2 = 0.9418 and q 2 = 0.9349 for the validation set. Structural indicators (alerts) defined as molecular fragments for the increase/decrease in the solute polarity parameter p were defined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. Snyder LR, Kirkland JJ, Dolan JW (2009) Introduction to modern liquid chromatography, 3rd edn. Wiley, New York

    Book  Google Scholar 

  2. Poole CF (2003) The essence of chromatography. Elsevier, Amsterdam

    Google Scholar 

  3. Rosés M, Bosch E (1993) Anal Chim Acta 274:147–162

    Article  Google Scholar 

  4. Bosch E, Bou P, Rosés M (1994) Anal Chim Acta 299:219–229

    Article  CAS  Google Scholar 

  5. Rosés M, Bolliet D, Poole CF (1998) J Chromatogr A 829:29–40

    Article  Google Scholar 

  6. Torres-Lapasio JR, Garcia-Alvarez-Coque MC, Rosés M, Bosch E (2002) J Chromatogr A 955:19–34

    Article  CAS  Google Scholar 

  7. Torres-Lapasio JR, Garcia-Alvarez-Coque MC, Rosés M, Bosch E, Zissimos AM, Abraham MH (2004) Anal Chim Acta 515:209–227

    Article  CAS  Google Scholar 

  8. Katritzky AR, Kuanar M, Slavov S, Hall CD, Karelson M, Kahn I, Dobchev DA (2010) Chem Rev 110:5714–5789

    Article  CAS  Google Scholar 

  9. Katritzky AR, Lobanov VS, Karelson M (1995) Chem Soc Rev 24:279–287

    Article  CAS  Google Scholar 

  10. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2008) QSAR Comb Sci 27:432–436

    Article  CAS  Google Scholar 

  11. Mercader AG, Duchowicz PR, Fernández FM, Castro EA (2010) J Chem Inf Model 50:1542–1548

    Article  CAS  Google Scholar 

  12. Héberger KJ (2007) Chromatogr A 1158:273–305

    Article  Google Scholar 

  13. Kaliszan R (2007) Chem Rev 107:3212–3246

    Article  CAS  Google Scholar 

  14. Bonchev DJ (2000) Chem Inf Comp Sci 40:934–941

    Article  CAS  Google Scholar 

  15. Estrada E, González HJ (2003) Chem Inf Comp Sci 43:75–84

    Article  CAS  Google Scholar 

  16. Ivanciuc O (2013) Curr Comput Aid Drug 9:153–163

    Article  CAS  Google Scholar 

  17. Toropov AA, Toropova AP, Benfenati E, Gini G (2013) Curr Comput Aid Drug 9:226–232

    Article  CAS  Google Scholar 

  18. Daylight Chemical Information Systems, Inc (2008) http://www.daylight.com (accessed 11.02.14)

  19. Toropov AA, Toropova AP, Benfenati E (2010) Eur J Med Chem 45:3581–3587

    Article  CAS  Google Scholar 

  20. Toropov AA, Toropova AP, Rasulev BF, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2012) J Comput Chem 33:1902–1906

    Article  CAS  Google Scholar 

  21. Toropov AA, Toropova AP, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) Chemosphere 90:877–880

    Article  CAS  Google Scholar 

  22. Toropova AP, Toropov AA, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) J Math Chem 51:1684–1693

    Article  CAS  Google Scholar 

  23. Bosque R, Sales J, Bosch E, Rosés M, García-Alvarez-Coque MC, Torres-Lapasió JR (2003) J Chem Inf Comp Sci 43:1240–1247

    Article  CAS  Google Scholar 

  24. Weininger D (1988) J Chem Inf Comp Sci 28:31–36

    Article  CAS  Google Scholar 

  25. Weininger D, Weininger A, Weininger JL (1989) J Chem Inf Comp Sci 29:97–101

    Article  CAS  Google Scholar 

  26. Weininger D (1990) J Chem Inf Comp Sci 30:237–243

    Article  CAS  Google Scholar 

  27. Toropova AP, Toropov AA, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2011) J Comput Chem 32:2727–2733

    Article  CAS  Google Scholar 

  28. Veselinović AM, Milosavljević JB, Toropov AA, Nikolić GM (2013) Eur J Pharm Sci 48:532–541

    Article  Google Scholar 

  29. Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) Chemosphere 92:31–37

    Article  CAS  Google Scholar 

  30. Roy K (2007) Expert Opin Drug Dis 2:1567–1577

    Article  CAS  Google Scholar 

  31. Roy PP, Leonard JT, Roy K (2008) Chemom Intell Lab Syst 90:31–42

    Article  CAS  Google Scholar 

  32. Golbraikh A, Tropsha A (2002) J Mol Graph Model 20:269–276

    Article  CAS  Google Scholar 

  33. Roy PP, Roy K (2009) Chem Biol Drug Des 73:442–455

    Article  CAS  Google Scholar 

  34. Ojha PK, Mitra I, Das RN, Roy K (2011) Chemometr Intell Lab 107:194–205

    Article  CAS  Google Scholar 

  35. Ojha PK, Roy K (2011) Chemometr Intell Lab 109:146–161

    Article  CAS  Google Scholar 

  36. Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) J Chem Inf Model 52:396–408

    Article  CAS  Google Scholar 

  37. Tropsha A (2010) Mol Inf 29:476–488

    Article  CAS  Google Scholar 

  38. Toropov AA, Toropova AP, Benfenati E, Fanelli R (2013) Struct Chem 24:1369–1381

    Article  CAS  Google Scholar 

  39. Organisation for Economic Co-operation and Development (2007) Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models. OECD Series on Testing and Assessment 69. OECD Document ENV/JM/MONO (2007) 2

Download references

Acknowledgments

This work has been supported by the Ministry of Education, Science and Technological Development, the Republic of Serbia, under Project Number 31060. APT and AAT acknowledge support from the EC project NANOPUZZLES (Project Reference: 309837), EU FP7 project PreNanoTox (contract 309666), and EC project CALEIDOS (the project number LIFE11-INV/IT 00295).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aleksandar M. Veselinović.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 119 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Veselinović, A.M., Veselinović, J.B., Nikolić, G.M. et al. QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method. Struct Chem 27, 821–828 (2016). https://doi.org/10.1007/s11224-015-0636-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-015-0636-2

Keywords

Navigation