Journal of Computer-Aided Molecular Design

, Volume 8, Issue 4, pp 405–420 | Cite as

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

  • Jonathan D. Hirst
  • Ross D. King
  • Michael J. E. Sternberg
Research Papers

Summary

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substitured benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

Key words

QSAR Artificial intelligence Neural networks DHFR inhibitors 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hansch, C., Maloney, P.P., Fujita, T. and Muir, R.M., Nature, 194 (1962) 178.Google Scholar
  2. 2.
    Hansch, C., Acc. Chem. Res., 2 (1969) 232.Google Scholar
  3. 3.
    So, S.-S. and Richards, W.G., J. Med. Chem., 35 (1992) 3201.Google Scholar
  4. 4.
    Andrea, T.A. and Kalayeh, H., J. Med. Chem., 34 (1991) 2824.Google Scholar
  5. 5.
    Aoyama, T., Suzuki, Y. and Ichikawa, H., J. Med. Chem., 33 (1990) 905.Google Scholar
  6. 6.
    Aoyama, T. and Ichikawa, H., J. Chem. Inf. Comput. Sic., 32 (1992) 492.Google Scholar
  7. 7.
    Tetko, I.V., Luik, A.I. and Poda, G.I., J. Med. Chem., 36 (1993) 811.Google Scholar
  8. 8.
    King, R.D., Muggleton, S., Lewis, R.A. and Sternberg, M.J.E., Proc. Natl. Acad. Sci. USA, 89 (1992) 11322.Google Scholar
  9. 9.
    Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421.Google Scholar
  10. 10.
    Li, R.L., Hansch, C. and Kaufman, B.T., J. Med. Chem., 25 (1982) 435.Google Scholar
  11. 11.
    Champness, J.N., Stammers, D.K. and Beddell, C.R., FEBS Lett., 199 (1986) 61.Google Scholar
  12. 12.
    Matthews, D.A., Bolin, J.T., Burridge, J.M., Filman, D.J., Volz, K.W., Kaufman, B.T., Beddell, C.R., Champness, J.N., Stammers, D.K. and Kraut, J., J. Biol. Chem., 260 (1985) 381.Google Scholar
  13. 13.
    Selassie, C.D., Li, R.-L., Poe, M. and Hansch, C., J. Med. Chem., 34 (1991) 46.Google Scholar
  14. 14.
    Hansch, C., Li, R.-I., Blaney, J.M. and Langridge, R., J. Med. Chem., 25 (1982) 777.Google Scholar
  15. 15.
    Li, R.-L. and Poe, M., J. Med. Chem., 31 (1988) 366.Google Scholar
  16. 16.
    Dietrich, S.W., Blaney, J.M., Reynolds, M.A., Jow, P.Y.C. and Hansch, C., J. Med. Chem., 23 (1980) 1205.Google Scholar
  17. 17.
    Roth, B., Aig, E., Rauckman, B.S., Srelitz, J.Z., Phillips, A.P., Ferone, R., Bushby, S.R.M. and Siegel, C.W., J. Med. Chem., 24 (1981) 933.Google Scholar
  18. 18.
    Roth, B., Rauckman, B.S., Ferone, R., Baccanari, D.P., Champness, J.N. and Hyde, R.M., J. Med. Chem., 30 (1987) 348.Google Scholar
  19. 19.
    Leo, A., Hansch, C. and Elkins, D., Chem. Rev., 71 (1971) 525.Google Scholar
  20. 20.
    Muggleton, S. and Feng, C., In Arikawa, S., Goto, S., Ohsuga, S. and Yokomori, T. (Eds.) Proceedings of the First Conference on Algorithmic Learning Theory, Japanese Society of Artificial Intelligence, Ohmsha Press, Tokyo, 1990, pp. 368–381.Google Scholar
  21. 21.
    Minitab, release 7.2, VAX/VMS version, Minitab, Inc., Pensylvania State University, Philadelphia, PA, 1989.Google Scholar
  22. 22.
    Rumelhart, D.E., Hinton, G.E. and Williams, R.J., Nature, 323 (1986) 533.Google Scholar
  23. 23.
    Owens, A.J. and Filkin, D.L., In IEEE/INNS International Joint Conference of Neural Networks, Washington, DC, 1989, pp. 381–386.Google Scholar
  24. 24.
    Gear, C.W., Numerical Initial Value Problems in Ordinary Differential Equations, Prentice Hall, Englewood Cliffs, NJ, 1971.Google Scholar
  25. 25.
    Livingstone, D.J. and Salt, D.W., Bioorg. Med. Chem. Lett., 2 (1992) 213.Google Scholar
  26. 26.
    Livingstone, D.J. and Mallanack, D.T., J. Med. Chem., 36 (1993) 1295.Google Scholar
  27. 27.
    DeLong, H., A Profile of Mathematical Logic, Addison-Wesley, Reading, MA, 1970.Google Scholar
  28. 28.
    David, H.A., Biometrika, 74 (1987) 432.Google Scholar
  29. 29.
    Muggleton, S., Srinivasan, A. and Bain, M., In Sleeman, D. and Edwards, P. (Eds.) Proceedings of the 9th International Conference on Machine Learning, Morgan-Kaufman, San Mateo, CA, 1992, pp. 338–347.Google Scholar
  30. 30.
    Kendall, M. and Stuart, A., The Advanced Theory of Statistics, Griffen, London, 1977.Google Scholar
  31. 31.
    Press, W.H., Teukolsky, S.A., Vettering, W.T. and Flannery, B.P., Numerical Recipes, Cambridge University Press, Cambridge, 1992.Google Scholar

Copyright information

© ESCOM Science Publishers B.V 1994

Authors and Affiliations

  • Jonathan D. Hirst
    • 1
  • Ross D. King
    • 1
  • Michael J. E. Sternberg
    • 1
  1. 1.Biomolecular Modelling LaboratoryImperial Cancer Research FundLondonU.K.

Personalised recommendations