Skip to main content
Log in

The index of ideality of correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers

  • SHORT COMMUNICATION
  • Published:
Journal of Polymer Research Aims and scope Submit manuscript

Abstract

Glass transition temperatures of polymers were modelled by means of the CORAL software available on the Internet (http://www.insilico.eu/coral). The architecture of monomers was represented via simplified molecular input line entry systems (SMILES). Three random splits into the training and validation sets were tested to build up quantitative structure - property relationships (QSPRs). The index of Ideality of Correlation (IIC) represents a new measure of predictive potential. Application of the IIC as the criterion of predictive potential for the calibration set was tested in this work and resulted in correct recommendations for selection of the best model from three different models considered here.

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

References

  1. Veselinović JB, Đorđević V, Bogdanović M, Morić I, Veselinović AM (2018) QSAR modeling of dihydrofolate reductase inhibitors as a therapeutic target for multiresistant bacteria. Struct Chem 29(2):541–551

    Article  Google Scholar 

  2. Kovalishyn V, Abramenko N, Kopernyk I, Charochkina L, Metelytsia L, Tetko IV, Peijnenburg W, Kustov L (2018) Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform. Food Chem Toxicol 112:507–517

    Article  CAS  Google Scholar 

  3. Duchowicz PR, Bacelo DE, Fioressi SE, Palermo V, Ibezim NE, Romanelli GP (2018) QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors. Med Chem Res 27(2):420–428

    Article  CAS  Google Scholar 

  4. Mettou A, Papaneophytou C, Melagraki G, Maranti A, Liepouri F, Alexiou P, Papakyriakou A, Couladouros E, Eliopoulos E, Afantitis A, Kontopidis G (2018) Aqueous Solubility Enhancement for Bioassays of Insoluble Inhibitors and QSPR Analysis: A TNF-α Study. SLAS Discov 23(1):84–93

    CAS  PubMed  Google Scholar 

  5. Amata E, Marrazzo A, Dichiara M, Modica MN, Salerno L, Prezzavento O, Nastasi G, Rescifina A, Romeo G, Pittalà V (2017) Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors. Data Brief 15:281–299

    Article  Google Scholar 

  6. Sokolović D, Ranković J, Stanković V, Stefanović R, Karaleić S, Mekić B, Milenković V, Kocić J, Veselinović AM (2017) QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Med Chem Res 26(4):796–804

    Article  Google Scholar 

  7. Mercader AG, Bacelo DE, Duchowicz PR (2017) Different encoding alternatives for the prediction of halogenated polymers glass transition temperature by quantitative structure–property relationships. Int J Polym Anal Ch 22(7):639–648

    Article  CAS  Google Scholar 

  8. Yu X (2010) Support vector machine-based QSPR for the prediction of glass transition temperatures of polymers. Fiber Polym 11(5):757–766

    Article  CAS  Google Scholar 

  9. Barbosa-Da-Silva R, Stefani R (2013) QSPR based on support vector machines to predict the glass transition temperature of compounds used in manufacturing OLEDs. Mol Simulat 39(3):234–244

    Article  CAS  Google Scholar 

  10. Zohari N, Sheibani N, Chavoshi HZ (2018) Investigation of the most effective molecular descriptors on the thermal behaviour of energetic azido-ester plasticizers through QSPR approach. J Therm Anal Calorim 131(3):3157–3167

    Article  CAS  Google Scholar 

  11. Chen M, Jabeen F, Rasulev B, Ossowski M, Boudjouk P (2018) A computational structure-property relationship study of glass transition temperatures for a diverse set of polymers. J Polym Sci B Polym Phys 56:877–885

    Article  CAS  Google Scholar 

  12. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36

    Article  CAS  Google Scholar 

  13. Toropov AA, Toropova AP (2015) Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere 124(1):40–46

    Article  CAS  Google Scholar 

  14. Toropova AP, Toropov AA (2014) CORAL software: Prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur J Pharm Sci 52(1):21–25

    Article  CAS  Google Scholar 

  15. Toropova AP, Toropov AA (2017) The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? Sci Total Environ 586:466–472

    Article  CAS  Google Scholar 

  16. Toropov AA, Toropova AP (2017) The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? Mutat Res Genet Toxicol Environ Mutagen 819:31–37

    Article  CAS  Google Scholar 

  17. Toropov AA, Carbó-Dorca R, Toropova AP (2018) Index of Ideality of Correlation: new possibilities to validate QSAR: a case study. Struct Chem 29(1):33–38

    Article  CAS  Google Scholar 

  18. Stoičkov V, Stojanović D, Tasić I, Šarić S, Radenković D, Babović P, Sokolović D, Veselinović AM (2018) QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method. Struct Chem 29(2):441–449

    Article  Google Scholar 

  19. Hawkins DM, Basak SC, Mills D (2003) Assessing Model Fit by Cross-Validation. J Chem Inf Comput Sci 43(2):579–586

    Article  CAS  Google Scholar 

  20. Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring rm2 metrics for validation of QSPR models. Chemometr Intell Lab Syst 107(1):194–205

    Article  CAS  Google Scholar 

  21. I-Kuei Lin L (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1):255–268

    Article  Google Scholar 

  22. Toropov AA, Toropova AP, Benfenati E, Leszczynska D, Leszczynski J (2010) SMILES-based optimal descriptors: QSAR analysis of fullerene-based HIV-1 PR inhibitors by means of balance of correlations. J Comput Chem 31(2):381–392

    CAS  PubMed  Google Scholar 

  23. Kumar P, Kumar A (2018)  Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles. Drug Res 68(4):189–195

    Article  CAS  Google Scholar 

  24. Toropova AP, Toropov AA, Kudyshkin VO, Leszczynska D, Leszczynski J (2014) Optimal descriptors as a tool to predict the thermal decomposition of polymers. J Math Chem 52(5):1171–1181

    Article  CAS  Google Scholar 

  25. Achary PGR, Begum S, Toropova AP, Toropov AA (2016) A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization. Materials Discovery 5:22–28

    Article  Google Scholar 

  26. Toropov AA, Toropova AP, Begum S, Achary PGR (2016) Towards predicting the solubility of CO2and N2in different polymers using a quasi-SMILES based QSPR approach. SAR QSAR Environ Res 27(4):293–301

    Article  CAS  Google Scholar 

  27. Yi L, Li C, Huang W, Yan D (2014) Soluble aromatic polyimides with high glass transition temperature from benzidine containing tert-butyl groups. J Polym Res 21(11):10

    Article  Google Scholar 

  28. Prasitnok K (2016) A coarse-grained model for polylactide: glass transition temperature and conformational properties. J Polym res 23(7):art no 139

  29. Javadi A, Shockravi A, Shourkaei FA, Koohgard M, Malek A (2018) Highly refractive thiazole-containing polyimides: a structural property comparison. J Polym Res 25(4):art no 99

  30. CROW, polymer sciences, http://polymerdatabase.com/index.html. Accessed 5 Sept 2018

Download references

Acknowledgements

APT, AAT are grateful for the contribution of the EU project LIFE-COMBASE (LIFE15 ENV/ES/000416). D.L. and J.L were supported by the NSF CREST Interdisciplinary Nanotoxicity Center Grant # HRD- 1547754.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alla P. Toropova.

Electronic supplementary material

ESM 1

(XLSX 23 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toropova, A.P., Toropov, A.A., Leszczynska, D. et al. The index of ideality of correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers. J Polym Res 25, 221 (2018). https://doi.org/10.1007/s10965-018-1618-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10965-018-1618-z

Keywords

Navigation