Journal of Computer-Aided Molecular Design

, Volume 19, Issue 11, pp 771–789 | Cite as

Genetic neural network modeling of the selective inhibition of the intermediate-conductance Ca2+-activated K+ channel by some triarylmethanes using topological charge indexes descriptors

  • Julio Caballero
  • Miguel Garriga
  • Michael FernándezEmail author


Selective inhibition of the intermediate-conductance Ca2+-activated K+ channel (IK Ca) by some clotrimazole analogs has been successfully modeled using topological charge indexes (TCI) and genetic neural networks (GNNs). A neural network monitoring scheme evidenced a highly non-linear dependence between the IK Ca blocking activity and TCI descriptors. Suitable subsets of descriptors were selected by means of genetic algorithm. Bayesian regularization was implemented in the network training function with the aim of assuring good generalization qualities to the predictors. GNNs were able to yield a reliable predictor that explained about 97% data variance with good predictive ability. On the contrary, the best multivariate linear equation with descriptors selected by linear genetic search, only explained about 60%. In spite of when using the descriptors from the linear equations to train neural networks yielded higher fitted models, such networks were very unstable and had relative low predictive ability. However, the best GNN BRANN 2 had a Q 2 of LOO of cross-validation equal to 0.901 and at the same time exhibited outstanding stability when calculating 80 randomly constructed training/test sets partitions. Our model suggested that structural fragments of size three and seven have relevant influence on the inhibitory potency of the studied IK Ca channel blockers. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (KSOM) built using the inputs of the best neural network predictor.


Bayesian regularization clotrimazole genetic algorithm ion channel neural networks QSAR 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gárdos G., (1958) Biophys. Acta. 30: 653CrossRefGoogle Scholar
  2. 2.
    Cook, N.S. and Quast, U., In Potassium Channels, Cook, N.S., Chichester, p. 70Google Scholar
  3. 3.
    Haylett, D.G. and Jenkinson, D.H., In Potassium Channels, Cook, N.S., Chichester, 1990, p. 70Google Scholar
  4. 4.
    Castle N.A., (1999) Perspect. Drug Discovery Des. 15: 131CrossRefGoogle Scholar
  5. 5.
    Vergara C., LaTorre R., Marrion N.V., Adelman J.P., (1998) Curr. Opin. Neurobiol. 8: 321CrossRefGoogle Scholar
  6. 6.
    Wulff H., Miller M.J., Hänsel W., Grissmer S., Cahalan M.D., Chandy K.G., (2000) Proc. Natl. Acad. Sci. 97: 8151CrossRefGoogle Scholar
  7. 7.
    Roxburgh C.J., Ganellin C.R., Athmani S., Bisi A., Quaglia W., Benton D.C.H., Shiner M.A.R., Malik-Hall M., Haylett D.G., Jenkinson D.H., (2001) J. Med. Chem. 44: 3244CrossRefGoogle Scholar
  8. 8.
    Kubinyi H., 1993.QSAR: Hansch Analysis and Related Approaches, VCH, New York, Google Scholar
  9. 9.
    Gálvez J., Garcia R., Salabert M.T., Soler R., (1994) J. Chem. Inf. Comput. Sci. 34: 520Google Scholar
  10. 10.
    Gálvez J., Garcia-Domenech R., de Julihn-Ortiz J.V., Soler R., (1995) J. Chem. Inf Comput. Sci. 35: 272CrossRefGoogle Scholar
  11. 11.
    Kios-Santamarina I., Garcia-Domenech R., Gálvez J., (1998) Bioorg. Med. Chem. Lett. 18: 477CrossRefGoogle Scholar
  12. 12.
    Calabuig C., Antón-Fos G.M., Gálvez J., García-Doménech R., (2004) Int. J. Pharm. 278: 111CrossRefGoogle Scholar
  13. 13.
    González M.P., Terán C., (2004) Bull. Math. Biol. 66: 907CrossRefGoogle Scholar
  14. 14.
    González M.P., Terán C., (2004) Bioorg. Med. Chem. Lett. 14: 3077CrossRefGoogle Scholar
  15. 15.
    Fernández M., Caballero J., Helguera A.M., Castro E.A., González M.P., (2005) Bioorg. Med. Chem. 13: 3269CrossRefGoogle Scholar
  16. 16.
    Fernández M., Tundidor-Camba A., Caballero J., (2005) Mol. Simulat. 31: 575CrossRefGoogle Scholar
  17. 17.
    González, M.P., Caballero, J., Garriga, M., González, G., Helguera, A.M. and Fernández, M., Bull. Math. Biol. (2005) (in press)Google Scholar
  18. 18.
    González, M.P., Caballero, J., Tundidor-Camba, A., Helguera, A.M., Fernández, M., Bioorg. Med. Chem. DOI: 10.1016/j.bmc.2005.08.009Google Scholar
  19. 19.
    Fernández, M. and Caballero, J., Bioorg. Med. Chem. DOI: 10.1016/j.bmc.2005.08.022Google Scholar
  20. 20.
    Caballero, J. and Fernández, M., J. Mol. Model. DOI: 10.1007/s00894-005-0014-xGoogle Scholar
  21. 21.
    So S.S., Karplus M., (1996) J. Med. Chem. 39: 1521CrossRefGoogle Scholar
  22. 22.
    So S.S., Karplus M., (1996) J. Med. Chem. 39: 5246CrossRefGoogle Scholar
  23. 23.
    Hemmateenejad B., Akhond M., Miri R., Shamsipur M., (2003) J. Chem. Inf. Comput. Sci. 43: 1328CrossRefGoogle Scholar
  24. 24.
    Takahata Y., Costa M.C.A., Gaudio A.C., (2003) J. Chem. Inf. Comput. Sci. 43: 540CrossRefGoogle Scholar
  25. 25.
    Hemmateenejad B., Safarpour M.A., Miri R., Nesari N., (2005) J. Chem. Inf. Model. 45: 190CrossRefGoogle Scholar
  26. 26.
    Hawkins D.M., (2004) J. Chem. Inf. Comput. Sci. 44: 44CrossRefGoogle Scholar
  27. 27.
    Kier L.B., Hall L.H., 1999 Molecular Structure Descriptors: The Electrotopological StateAcademic PressNew YorkGoogle Scholar
  28. 28.
    Hall L.H., Mohney B., Kier L.B., (1991) J. Chem. Inf. Comput. Sci. 31: 76Google Scholar
  29. 29.
    Girones X., Amat L., Robert D., Carbo-Dorca R., (2000) J. Comput. Aided Mol. Des. 14: 477CrossRefGoogle Scholar
  30. 30.
    Todeschini, R. and Consonni, V. and Pavan, M., DRAGON. version 2.1 (2003)Google Scholar
  31. 31.
    Holland J.H., 1975. Adaption in Natural and Artificial Systems The University of Michigan Press Ann Arbor, MIGoogle Scholar
  32. 32.
    Cartwright H.M., 1993. Applications of Artificial Intelligence in Chemistry Oxford University Press OxfordGoogle Scholar
  33. 33.
    The MathWorks Inc. MATLAB version 7.0. (2004), www.mathworks.comGoogle Scholar
  34. 34.
    The MathWorks Inc., 2004. Genetic Algorithm and Direct Search Toolbox User’s Guide for Use with MATLAB The Mathworks Inc. MassachusettsGoogle Scholar
  35. 35.
    Hertz J., Krogh A., Palmer R.G., 1991. Introduction to the Theory of Neural Computation Addison-Wesley Publishing Co. Redwood City, CAGoogle Scholar
  36. 36.
    Kolmogorov A.N., (1957) SSSR114: 953Google Scholar
  37. 37.
    The MathWorks Inc., 2004. Neural Network Toolbox User’s Guide for Use with MATLAB The Mathworks Inc. MassachusettsGoogle Scholar
  38. 38.
    Mackay D.J.C., (1992) Neural Comput 4: 415CrossRefGoogle Scholar
  39. 39.
    Burden F.R., Winkler D.A., (1999) J. Med. Chem. 42: 3183CrossRefGoogle Scholar
  40. 40.
    Burden F.R., Winkler D.A., (2000) Chem. Res. Toxicol. 13: 436CrossRefGoogle Scholar
  41. 41.
    Winkler D.A., Burden F.R., (2004) Biosilico 2: 104Google Scholar
  42. 42.
    Polley M.J., Winkler D.A., Burden F.R., (2004) J. Med. Chem.47:6230CrossRefGoogle Scholar
  43. 43.
    Kohonen T., (1982) Biol. Cybern. 43: 59CrossRefGoogle Scholar
  44. 44.
    Gasteiger J., Zupan J., (1995) Angew. Chem. Int. Ed. Engl. 32: 503CrossRefGoogle Scholar
  45. 45.
    Golbraikh A., Tropsha A., (2002) J. Mol. Graph. Model. 20: 269CrossRefGoogle Scholar
  46. 46.
    Zahouily M., Bazoui A.R., Sebti S., Zakarya D., (2002) J. Mol. Model. 8: 168CrossRefGoogle Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Julio Caballero
    • 1
  • Miguel Garriga
    • 2
  • Michael Fernández
    • 1
    Email author
  1. 1.Molecular Modeling Group, Center for Biotechnological Studies, Faculty of AgronomyUniversity of␣MatanzasMatanzasCuba
  2. 2.Plant Biotechnology Group, Center for Biotechnological Studies, Faculty of AgronomyUniversity of MatanzasMatanzasCuba

Personalised recommendations