Russian Journal of Organic Chemistry

, Volume 50, Issue 4, pp 459–463 | Cite as

Structure-reactivity relationships in terms of the condensed graphs of reactions

  • T. I. Madzhidov
  • P. G. Polishchuk
  • R. I. Nugmanov
  • A. V. Bodrov
  • A. I. Lin
  • I. I. Baskin
  • A. A. Varnek
  • I. S. Antipin
Article

Abstract

An approach for the prediction of rate constants of chemical reactions, based on the representation of a chemical reaction as a condensed graph, has been tested on more than 1000 bimolecular nucleophilic substitution reactions with neutral nucleophiles in 38 solvents. Molecular fragment descriptors, temperature, and solvent parameters characterizing solvation power have been used in the reaction modeling. The obtained models ensure a good correlation between the predicted and experimental values; the corresponding deviations are comparable with interlaboratory measurement errors.

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References

  1. 1.
    Recent Advances in QSAR Studies: Methods and Applications, Puzyn, T., Leszczynski, J., and Cronin, M.T.D., Eds., Dordrecht: Springer, 2010.Google Scholar
  2. 2.
    Statistical Modelling of Molecular Descriptors in QSAR/QSPR, Dehmer, M., Varmuza, K., and Bonchev, D., Eds., Weinheim: Wiley-Blackwell, 2012.Google Scholar
  3. 3.
    Kubinyi, H., QSAR: Hansch Analysis and Related Approaches, Weinheim: VCH, 1993.CrossRefGoogle Scholar
  4. 4.
    Halberstam, N.M., Baskin, I.I., Palyulin, V.A., and Zefirov, N.S., Mendeleev Commun., 2002, vol. 12, p. 185.CrossRefGoogle Scholar
  5. 5.
    Zhokhova, N.I., Baskin, I.I., Palyulin, V.A., Zefirov, A.N., and Zefirov, N.S., Dokl. Chem., 2007, vol. 417, p. 282.CrossRefGoogle Scholar
  6. 6.
    Kravtsov, A.A., Karpov, P.V., Baskin, I.I., Palyulin, V.A., and Zefirov, N.S., Dokl. Chem., 2011, vol. 441, p. 314.CrossRefGoogle Scholar
  7. 7.
    Vlédutz, G.É., Inform. Storage Retr., 1963, vol. 1, p. 117.CrossRefGoogle Scholar
  8. 8.
    Fujita, S., J. Chem. Inf. Comput. Sci., 1986, vol. 26, p. 205.CrossRefGoogle Scholar
  9. 9.
    Jauffret, P., Tonnelier, C., and Kaufmann, G., New J. Chem., 1990, vol. 14, p. 945.Google Scholar
  10. 10.
    Varnek, A., Fourches, D., Hoonakker, F., and Solov’ev, V.P., J. Comput.-Aided Mol. Des., 2005, vol. 19, p. 693.CrossRefGoogle Scholar
  11. 11.
    Varnek, A., Chemoinformatics and Computational Chemical Biology, Bajorath, J., Ed., New York: Humana, 2010.Google Scholar
  12. 12.
    Muller, C., Marcou, G., Horvath, D., Aires-de-Sousa, J., and Varnek, A., J. Chem. Inf. Model., 2012, vol. 52, p. 3116.CrossRefGoogle Scholar
  13. 13.
    Hoonakker, F., Lachiche, N., Varnek, A., and Wagner, A., Int. J. Artif. Intell. Tools, 2011, vol. 20, p. 253.CrossRefGoogle Scholar
  14. 14.
    Tablitsy konstant skorosti i ravnovesiya geteroliticheskikh organicheskikh reaktsii (Tabulated Rate and Equilibrium Constants of Heterolytic Organic Reactions), Pal’m, V.A., Ed., Moscow: VINITI, 1977, vol. 2.Google Scholar
  15. 15.
    Kuz’min, V.E., Artemenko, A.G., Polischuk, P.G., Muratov, E.N., Khromov, A.I., Liahovskiy, A.V., Andronati, S.A., and Makan, S.Y., J. Mol. Model., 2005, vol. 11, p. 457.CrossRefGoogle Scholar
  16. 16.
    Breiman, L., Machine Learning, 2001, vol. 45, p. 5.CrossRefGoogle Scholar
  17. 17.
    Kravtsov, A.A., Karpov, P.V., Baskin, I.I., Palyulin, V.A., and Zefirov, N.S., Dokl. Chem., 2011, vol. 440, p. 299.CrossRefGoogle Scholar
  18. 18.
    InstantJChem 6.0.6, 2013, ChemAxon. http://www.chemaxon.com
  19. 19.
    JChem 6.0.6, 2013, ChemAxon. http://www.chemaxon.com
  20. 20.
    Fontain, E., Anal. Chim. Acta, 1992, vol. 265, p. 227.CrossRefGoogle Scholar
  21. 21.
    Marcou, G., Solov’ev, V., Horvath, D., and Varnek, A., ISIDA Fragmentor2011. User Manual. 2012. http://infochim.u-strasbg.fr/recherche/Download/Fragmentor/Fragmentor2011-Manual.pdf
  22. 22.
    Polishchuk, P., SIRMS. 2013, http://github.com/DrrDom/sirms
  23. 23.
    Catalán, J., López, V., Pérez, P., Martin-Villamil, R., and Rodriguez, J.G., Justus Liebigs Ann. Chem., 1995, p. 241.Google Scholar
  24. 24.
    Catalán, J. and Díaz, C., Justus Liebigs Ann. Chem., 1997, p. 1941.Google Scholar
  25. 25.
    Catalán, J., Díaz, C., López, V., Pérez, P., de Paz, J.L.G., and Rodríguez, J.G., Justus Liebigs Ann. Chem., 1996, p. 1785.Google Scholar
  26. 26.
    Taft, R.W. and Kamlet, M.J., J. Am. Chem. Soc., 1976, vol. 98, p. 2886.CrossRefGoogle Scholar
  27. 27.
    Kamlet, M.J. and Taft, R.W., J. Am. Chem. Soc., 1976, vol. 98, p. 377.CrossRefGoogle Scholar
  28. 28.
    Kamlet, M.J., Abboud, J.L., and Taft, R.W., J. Am. Chem. Soc., 1977, vol. 99, p. 6027.CrossRefGoogle Scholar
  29. 29.
    Liaw, A. and Wiener, M., R News, 2002, vol. 2, p. 18.Google Scholar
  30. 30.
    R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2012.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2014

Authors and Affiliations

  • T. I. Madzhidov
    • 1
  • P. G. Polishchuk
    • 2
  • R. I. Nugmanov
    • 1
  • A. V. Bodrov
    • 1
  • A. I. Lin
    • 1
  • I. I. Baskin
    • 3
    • 4
  • A. A. Varnek
    • 4
  • I. S. Antipin
    • 1
  1. 1.Kazan (Volga Region) Federal UniversityKazanTatarstan, Russia
  2. 2.Bogatskii Physicochemical InstituteNational Academy of Sciences of UkraineOdessaUkraine
  3. 3.Faculty of ChemistryMoscow State UniversityMoscowRussia
  4. 4.Chemoinformatics LaboratoryUniversity of StrasbourgStrasbourgFrance

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