Skip to main content
SpringerLink
Log in

Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

The presence of polyhalogenated persistent organic pollutants (POPs), such as Cl/Br-substituted benzenes, biphenyls, diphenyl ethers, and naphthalenes has been identified in all environmental compartments. The exposure to these compounds can pose potential risk not only for ecological systems, but also for human health. Therefore, efficient tools for comprehensive environmental risk assessment for POPs are required. Among the factors vital for environmental transport and fate processes is melting point of a compound. In this study, we estimated the melting points of a large group (1419 compounds) of chloro- and bromo- derivatives of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenylethers, and benzenes by utilizing quantitative structure—property relationship (QSPR) techniques. The compounds were classified by applying structure-based clustering methods followed by GA-PLS modeling. In addition, random forest method has been applied to develop more general models. Factors responsible for melting point behavior and predictive ability of each method were discussed.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. de Lima Ribeiro FA, Ferreira M (2003) QSPR models of boiling point, octanol–water partition coefficient and retention time index of polycyclic aromatic hydrocarbons. J Mol Struc 663:109–126

    Article  Google Scholar 

  2. Nadal M, Espinosa G, Schuhmacher M, Domingo JL (2004) Patterns of PCDDs and PCDFs in human milk and food and their characterization by artificial neural networks. Chemosphere 54:1375–1382

    Article  CAS  Google Scholar 

  3. Ding G, Chen J, Qiao X, Huang L, Lin J, Chen X (2006) Quantitative relationships between molecular structures, environmental temperatures and solid vapor pressures of PCDD/Fs. Chemosphere 62:1057–1063

    Article  CAS  Google Scholar 

  4. Xu HY, Zou JW, Yu QS, Wang YH, Zhang JY, Jin HX (2007) QSPR/QSAR models for prediction of the physicochemical properties and biological activity of polybrominated diphenyl ethers. Chemosphere 66:1998–2010

    Article  CAS  Google Scholar 

  5. Ritter L, Solomon KR, Forget J, Stemeroff M, O'Leary C (1995) Persistent organic pollutants: an assessment report, International Programme on Chemical Safety. World Health Organization, Canada

    Google Scholar 

  6. Balk S, Carpenter DO, Corra L, Diaz-Barriga MFR, Sly P, Ehrenstein OSV, Tirado MC (2010) Persistent organic pollutants: impact on child health. Geneva, Switzerland.

  7. Nicolopoulou-Stamati P, Pitsos MA (2001) The Impact of endocrine disrupters on the female reproductive system. Hum Reprod Update 7:323–330

    Article  CAS  Google Scholar 

  8. Katritzky AR, Maran U, Lobanov VS, Karelson M (2000) Structurally diverse quantitative structure–property relationship correlations of technologically relevant physical properties. J Chem Inf Comput Sci 40:1–18

    Article  CAS  Google Scholar 

  9. Vasseur P, Cossu-Leguille C (2006) Linking molecular interactions to consequent effects of persistent organic pollutants (POPs) upon populations. Chemosphere 62:1033–1042

    Article  CAS  Google Scholar 

  10. Katritzky AR, Maran U, Karelson M, Lobanov VS (1997) Prediction of melting points for the substituted benzenes: a QSPR approach. J Chem Inf Comp Sci 37:913–919

  11. Katritzky AR, Lomaka A, Petrukhin R, Jain R, Karelson M, Visser AE, Rogers RD (2002) QSPR correlation of the melting point for pyridinium bromides, potential ionic liquids. J Chem Inf Comp Sci 42:71–74

    Article  CAS  Google Scholar 

  12. Schultz TW, Cronin MTD, Walker JD, Aptula AO (2003) Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective. J Mol Struct 622:1–22

    Article  CAS  Google Scholar 

  13. Bolboaca SD, Jäntschi L (2013) Quantitative structure-activity relationships: linear regression modelling and validation strategies by example. Biomath 2(1):1–11

    Article  Google Scholar 

  14. Asadollahi T, Dadfarnia S, Shabani AM, Ghasemi JB, Sarkhosh M (2011) QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening. Molecules 16:1928–1955

    Article  CAS  Google Scholar 

  15. Gramatica P, Consolaro F, Pozzi S (2001) QSAR approach to POPs screening for atmospheric persistence. Chemosphere 43:655–664

    Article  CAS  Google Scholar 

  16. Dudek AZ, Arodz T, Galvez J (2006) Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb Chem High T Scr 9:213–228

    CAS  Google Scholar 

  17. OECD (2014) A guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models. http://www.oecd.org/dataoecd/33/37/37849783.pdf (accessed July 13)

  18. Bhhatarai B, Teetz W, Liu T, Öberg T, Jeliazkova N, Kochev N, Pukalov O, Tetko IV, Kovarich S, Papa E, Gramatica P (2011) CADASTER QSPR models for predictions of melting and boiling points of perfluorinated chemicals. Mol Inf 30:189–204

    Article  CAS  Google Scholar 

  19. Trohalaki S, Pachter R, Drake GW, Hawkins T (2005) Quantitative structure–property relationships for melting points and densities of ionic liquids. Energy Fuels 19:279–284

    Article  CAS  Google Scholar 

  20. Katritzky AR, Jain R, Lomaka A, Petrukhin R, Maran U, Karelson M (2001) Perspective on the relationship between melting points and chemical structure. Cryst Growth Des 1:261–265

    Article  CAS  Google Scholar 

  21. Godavarthy SS, Robinson RL Jr, Gasem KAM (2006) An improved structure–property model for predicting melting-point temperatures. Ind Eng Chem Res 45:5117–5126

    Article  CAS  Google Scholar 

  22. Murugan R, Grendze MP, Toomey J, Katrizky A, Karelson M, Lobanov V, Rachwal P (1994) Predicting physical properties from molecular structure. Chemtech, Washington DC 24:17–17

    CAS  Google Scholar 

  23. Hanson M, Rouvray D (1987) The use of topological indices to estimate the melting points of organic molecules. Graph Theor Topol Chem 51:201–208

    CAS  Google Scholar 

  24. Abramowitz R, Yalkowsky S (1990) Melting point, boiling point, and symmetry. Pharm Res 7:942–947

    Article  CAS  Google Scholar 

  25. Dearden JC (1991) The QSAR prediction of melting point, a property of environmental relevance. Sci Total Environ 109–110:59–68

    Article  Google Scholar 

  26. Charton M, Charton B (1994) Quantitative description of structural effects on melting points of substituted alkanes. J Phys Org Chem 7:196–206

    Article  CAS  Google Scholar 

  27. Gramatica P, Navas N, Todeschini R (1998) 3D-modelling and prediction by WHIM descriptors. Part 9. Chromatographic relative retention time and physico-chemical properties of polychlorinated biphenyls (PCBs). Chemomet Intell Lab Syst 40(1):53–56

    Article  CAS  Google Scholar 

  28. Kušić H, Rasulev B, Leszczynska D, Leszczynski J, Koprivanac N (2009) Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: a QSAR study. Chemosphere 75:1128–1134

    Article  Google Scholar 

  29. Puzyn T, Gajewicz A, Rybacka A, Haranczyk M (2011) Global versus local QSPR models for persistent organic pollutants: balancing between predictivity and economy. Struct Chem 22:873–884

    Article  CAS  Google Scholar 

  30. Howard PH, Muir DC (2011) Identifying new persistent and bioaccumulative organics among chemicals in commerce II: pharmaceuticals. Environ Sci Tech 45:6938–6946

    Article  CAS  Google Scholar 

  31. Gajewicz A, Haranczyk M, Puzyn T (2011) Predicting logarithmic values of the subcooled liquid vapor pressure of halogenated persistent organic pollutants with QSPR: how different are chlorinated and brominated congeners? Atmos Environ 44:1428–1436

  32. Odziomek K, Gajewicz A, Haranczyk M, Puzyn T (2013) Reliability of environmental fate modeling results for POPs based on various methods of determining the air/water partition coefficient (log Kaw). Atmos Environ 73:177–184

    Article  CAS  Google Scholar 

  33. Puzyn T, Mostrag-Szlichtyng A, Gajewicz A, Skrzyński M, Worth AP (2011) Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models. Struct Chem 22:795–804

    Article  CAS  Google Scholar 

  34. Puzyn T, Mostrag-Szlichtyng A, Falandysz J, Kholod Y, Leszczynski J (2009) Predicting water solubility of congeners: chloronaphthalenes—a case study. J Hazard Mat 170:1014–1022

    Article  CAS  Google Scholar 

  35. Coleman WF, Arumainayagam CR (1998) HyperChem 5 (by Hypercube Inc). J Chem Ed 75:416

  36. Vargyas M, Papp J, Csizmadia F, Csepregi S, Papp A, Vadasz P (2008) Maximum common substructure based hierarchical clustering, Noordwijkerhout, Netherlands

  37. Mauri A, Consonni V, Pavan M, Todeschini R (2006) Dragon software: an easy approach to molecular descriptor calculations. Match 56: 237–248

  38. EigenVector Research (2014) EigenVector Research, I. PLS Toolbox, 7.8.2. http://www.eigenvector.com/

  39. MATLAB (2014) MATLAB (R2014a), version 8.3. Natick, MA

  40. Hasegawa K, Funatsu K (2000) Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships. SAR QSAR Environ Res 11:189–209

  41. Liaw A, News MWR (2002) Classification and regression by random forest. R News 2:18–22

  42. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  43. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701

    Article  CAS  Google Scholar 

  44. Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank the NSF CREST Interdisciplinary Nanotoxicity Center NSF-CREST – Grant # HRD-0833178; NSF-EPSCoR Award Number: 362492-190200-01\NSFEPS-0903787 for support. B.R. gratefully acknowledges support from the North Dakota State University Center for Computationally Assisted Science and Technology and the U.S. Department of Energy through Grant No. DE-SC0001717.

Author information

Author notes

    Authors and Affiliations

    1. Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, P.O. Box: 17910, Jackson, MS, USA

      Marquita Watkins, Natalia Sizochenko, Bakhtiyor Rasulev & Jerzy Leszczynski

    2. Center for Computationally Assisted Science and Technology, North Dakota State University, Fargo, ND, USA

      Bakhtiyor Rasulev

    Authors
    1. Marquita Watkins
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Natalia Sizochenko
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Bakhtiyor Rasulev
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Jerzy Leszczynski
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Jerzy Leszczynski.

    Additional information

    Marquita Watkins and Natalia Sizochenko contributed equally to this work.

    Electronic supplementary material

    Below is the link to the electronic supplementary material.

    ESM 1

    (PDF 1.25 mb)

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Watkins, M., Sizochenko, N., Rasulev, B. et al. Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach. J Mol Model 22, 55 (2016). https://doi.org/10.1007/s00894-016-2917-0

    Download citation

    • Received:

    • Accepted:

    • Published:

    • DOI: https://doi.org/10.1007/s00894-016-2917-0

    Keywords

    Search

    Navigation