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Quantitative structure-activity relationships of cyclin-dependent kinase 1 inhibitors

  • A. V. Zakharov
  • A. A. Lagunin
  • D. A. Filimonov
  • V. V. Poroikov
Bioinformatics

Abstract

A new approach for quantitative structure-activity relationships based on MNA descriptors, fuzzy gradation method and self-consistent regression has been proposed. This approach has been realized in the computer program GUSAR (General Unrestricted Structure Activity Relationships). The method has been validated using inhibitors of cyclin-dependent kinase 1 (CDK). Prediction accuracy is comparable with that of 3D QSAR: CoMFA and CoMSIA. However, in contrast to CoMFA and CoMSIA, GUSAR approach does not require information about 3D structure of an enzyme and a ligand. Application of GUSAR method for heterogeneous training sets containing chemical compounds from different chemical classes has been shown.

Key words

CDK1 inhibitors QSAR self-consistent regression computer prediction 

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References

  1. 1.
    Morgan, D.O., Nature, 1995, vol. 374, pp. 131–134.CrossRefGoogle Scholar
  2. 2.
    Wolfel, T., Hauer, M., Schneider, J., Serrano, M., Wolfel, C., Klehmann, H., De Plaen, E., Hankeln, T., Meyerzum-Buschenfelde, K., and Beach, D., Science, 1995, vol. 269, p. 1281.CrossRefGoogle Scholar
  3. 3.
    Livingstone, D., Data Analysis for Chemists. Applications to QSAR and Chemical Product Design, Oxford University Press, 1995.Google Scholar
  4. 4.
    Kubinyi, H., in QSAR and Molecular Modelling: Concepts, Computation Tools and Biological Applications, Sanz, F., Giraldo, J., and Manaut, F., Eds., Barcelona: J.P. Prous Science Publishers, 1995, pp. 2–18.Google Scholar
  5. 5.
    Kubinyi, H., 3D QSAR in Drug Design. Theory Methods and Applications, Leiden: ESCOM, 1993.Google Scholar
  6. 6.
    Leach, A., Molecular Modelling. Principles and Applications, Prentice Hall, 2001.Google Scholar
  7. 7.
    Gasteiger, J., Handbook of Chemoinformatics. From Data to Knowledge in 4 Volumes, Weinheim: WILEYVCH Verlag GmbH & Co. KgaA, 2003.Google Scholar
  8. 8.
    Filimonov, D., Poroikov, V., Borodina, Yu., Gloriozova, T., J. Chem. Inf. Comput. Sci., 1999, vol. 39, no. 4, pp. 666–670.CrossRefGoogle Scholar
  9. 9.
    Filimonov, D.A., Akimov, D.V., and Poroikov, V.V., Khim.-Farm. Zh., 2004, vol. 38, no. 1, pp. 21–24.Google Scholar
  10. 10.
    Sybyl, 6.3 ed.; SYBYL Molecular Modeling Software, Tripos Associates Ltd.: St. Louis, MO, 1992. (www.tripos.com).Google Scholar
  11. 11.
    Cramer, R.D., III, Patterson, D.E., and Bunce, J.D., J. Am. Chem. Soc., 1988, vol. 110, pp. 5959–5967.CrossRefGoogle Scholar
  12. 12.
    Klebe, G., Abraham, U., and Mietzner, T., J. Med. Chem., 1994, vol. 37, pp. 4130–4146.CrossRefGoogle Scholar
  13. 13.
    Ducrot, P., Legraverend, M., and David, S., J. Med. Chem., 2000, vol. 43, pp. 4098–4108.CrossRefGoogle Scholar
  14. 14.
    Kunick, C., Lauenroth, K., Wieking, K., Xu, X., Schultz, C., Gussio, R., Zaharevitz, D., Leost, M., Meijer, L., Weber, A., Flemming, S., and Lemcke, T., J. Med. Chem., 2004, vol. 47, pp. 22–36.CrossRefGoogle Scholar
  15. 15.
    Kunick, C., Zeng, Z., Gussio, R., Zaharevitz, D., Leost, M., Totzke, F., Schachtele, C., Kubbutat, M., Meijer, L., and Thomas, L., ChemBioChem., 2005, vol. 6, pp. 1–9.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2007

Authors and Affiliations

  • A. V. Zakharov
    • 1
  • A. A. Lagunin
    • 1
  • D. A. Filimonov
    • 1
  • V. V. Poroikov
    • 1
  1. 1.Institute of Biomedical ChemistryRussian Academy of Medical SciencesMoscowRussia

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