Skip to main content
SpringerLink
Log in

Applying the Monte Carlo technique to build up models of glass transition temperatures of diverse polymers

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

Abstract

Optimal descriptors calculated with SMILES represented a structure of monomer units applied to build up a model of glass transition temperatures of diverse polymers. Quantitative structure-property relationships (QSPRs) were established for the above dataset. The statistical quality of the model of glass transition temperatures is quite good. The simplified molecular input-line entry system (SMILES) has been used to represent the molecular structure of corresponding monomers. The hybrid optimal descriptors calculated with the so-called correlation weights of molecular features extracted from SMILES and molecular hydrogen-suppressed graph (HSG) were used as the basis of the one-variable model. The index of ideality of correlation (IIC) is a new criterion of the predictive potential of the QSPR model. Here, the applicability of the IIC as a tool to improve the predictive potential of the model for glass transition temperatures is confirmed.

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

Similar content being viewed by others

References

  1. 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. https://doi.org/10.1002/polb.24602

    Article  CAS  Google Scholar 

  2. Toropova AP, Toropov AA, Leszczynska D, Leszczynski J (2018) The index of ideality of correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers. J Polym Res 25(10):221. https://doi.org/10.1007/s10965-018-1618-z

    Article  CAS  Google Scholar 

  3. 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 29:541–551. https://doi.org/10.1007/s11224-017-1051-7

    Article  CAS  Google Scholar 

  4. Toropova AP, Toropov AA, Kudyshkin VO, Rallo R (2015) Prediction of the Q-e parameters from structures of transfer chain agents. J Polym Res 22:128. https://doi.org/10.1007/s10965-015-0778-3

    Article  CAS  Google Scholar 

  5. Nimbhal M, Bagri K, Kumar P, Kumar A (2020) The index of ideality of correlation: a statistical yardstick for better QSAR modeling of glucokinase activators. Struct Chem 31:831–839. https://doi.org/10.1007/s11224-019-01468-w

    Article  CAS  Google Scholar 

  6. Toropov AA, Toropova AP (2018) Improved model for biodegradability of organic compounds: the correlation contributions of rings. In: Bidoia E, Montagnolli R (eds) toxicity and biodegradation testing. Methods in pharmacology and toxicology. Humana Press, New York Chapter 8, pp 147–183

    Chapter  Google Scholar 

  7. 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. https://doi.org/10.1016/j.scitotenv.2017.01.198

    Article  CAS  PubMed  Google Scholar 

  8. Toropova AP, Toropov AA (2019) Does the index of ideality of correlation detect the better model correctly? Mol Inf 38:1800157. https://doi.org/10.1002/minf.201800157

    Article  CAS  Google Scholar 

  9. Kumar P, Kumar A, Sindhu J (2019) Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR. SAR QSAR Environ Res 30:63–80. https://doi.org/10.1080/1062936X.2018.1564067

    Article  CAS  PubMed  Google Scholar 

  10. Kumar P, Kumar A, Sindhu J, Lal S (2019) QSAR models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on Monte Carlo method. Drug Res 69:159–167. https://doi.org/10.1055/a-0652-5290

    Article  CAS  Google Scholar 

  11. Duchowicz PR, Fioressi SE, Bacelo DE, Saavedra LM, Toropova AP, Toropov AA (2015) QSPR studies on refractive indices of structurally heterogeneous polymers. Chemom Intell Lab Syst 140:86–91. https://doi.org/10.1016/j.chemolab.2014.11.008

    Article  CAS  Google Scholar 

  12. Golubović M, Lazarević M, Zlatanović D, Krtinić D, Stoičkov V, Mladenović B, Milić DJ, Sokolović D, Veselinović AM (2018) The anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study. Comput Biol Chem 75:32–38. https://doi.org/10.1016/j.compbiolchem.2018.04.009

    Article  CAS  PubMed  Google Scholar 

  13. Miccio LA, Schwartz GA (2020) From chemical structure to quantitative polymer properties prediction through convolutional neural networks. Polymer 193:122341. https://doi.org/10.1016/j.polymer.2020.122341

    Article  CAS  Google Scholar 

  14. Xu J, Wang L, Zhang H, Shen X, Liang G (2012) Quantitative structure-property relationships studies on free-radical polymerization chain-transfer constants for styrene. J Appl Polym Sci 123:356–364. https://doi.org/10.1002/app.34255

    Article  CAS  Google Scholar 

  15. Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14(5):1660–1701. https://doi.org/10.3390/molecules14051660

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

A.A.T. and A.P.T. express gratitude to the project LIFE-CONCERT (LIFE17 GIE/IT/000461) for the support.

Author information

Authors and Affiliations

  1. Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy

    Andrey A. Toropov & Alla P. Toropova

  2. Institute of Polymer Chemistry and Physics, Academy of Sciences of the Republic of Uzbekistan, Kodyri Street 7b, Tashkent, Uzbekistan, 100128

    Valentin O. Kudyshkin, Nurad I. Bozorov & Sayyora Sh. Rashidova

Authors
  1. Andrey A. Toropov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alla P. Toropova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Valentin O. Kudyshkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nurad I. Bozorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sayyora Sh. Rashidova
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally to this work.

Corresponding author

Correspondence to Alla P. Toropova.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 19 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toropov, A.A., Toropova, A.P., Kudyshkin, V.O. et al. Applying the Monte Carlo technique to build up models of glass transition temperatures of diverse polymers. Struct Chem 31, 1739–1743 (2020). https://doi.org/10.1007/s11224-020-01588-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-020-01588-8

Keywords

Search

Navigation