Perspectives in Drug Discovery and Design

, Volume 1, Issue 2, pp 279–290 | Cite as

New approaches to QSAR: Neural networks and machine learning

  • Ross D. King
  • Jonathan D. Hirst
  • Michael J. E. Sternberg
Perspectives Part I. Quantitative Structure-Activity Relationships

Summary

Neural networks and machine learning are two methods that are increasingly being used to model QSARs. They make few statistical assumptions and are nonlinear and nonparametric. We describe back-propagation from the field of neural networks, and GOLEM from machine learning, and illustrate their learning mechanisms using a simple expository problem. Back-propagation and GOLEM are then compared with multiple linear regression (using the parameters and their squares) on two real drug design problems: the inhibition ofEscherichia coli dihydrofolate reductase (DHFR) by pyrimidines and the inhibition of rat/mouse tumour DHFR by triazines.

Key words

Artificial intelligence Drug design 

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.
    Cramer III, R.D., Patterson, D.E. and Bunce, J.D., J. Am. Chem. Soc., 110 (1988) 5959.Google Scholar
  3. 3.
    Hansch, C., Li, R.-I., Blaney, J.M. and Langridge, R., J. Med. Chem., 25 (1982) 777.Google Scholar
  4. 4.
    Silipo, C. and Hansch, C., J. Am. Chem. Soc., 97 (1975) 6849.Google Scholar
  5. 5.
    Day, N. and Kerridge, D., Biometrics, 23 (1967) 313.Google Scholar
  6. 6.
    Friedman, J.H. and Stuetzle, W., J. Am. Stat. Assoc., 76 (1981) 817.Google Scholar
  7. 7.
    Weiss, S.M. and Kulikowski, C.A., Computer Systems that Learn, Morgan Kaufmann, San Mateo, CA, 1991.Google Scholar
  8. 8.
    Silverman, B., Density Estimation for Statistics, Chapman and Hall, New York, 1986.Google Scholar
  9. 9.
    Aoyama, T., Suzuki, Y. and Ichikawa, H., J. Med. Chem., 33 (1990) 905.Google Scholar
  10. 10.
    Aoyama, T. and Ichikawa, H., J. Chem. Inf. Comput. Sci., 32 (1992) 492.Google Scholar
  11. 11.
    Tetko, I.V., Luik, A.I. and Poda, G.I., J. Med. Chem., 36 (1993) 811.Google Scholar
  12. 12.
    Aoyama, T., Suzuki, Y. and Ichikawa, H., J. Med. Chem., 33 (1990) 2583.Google Scholar
  13. 13.
    So, S.-S. and Richards, W.G., J. Med. Chem., 35 (1992) 3201.Google Scholar
  14. 14.
    Andrea, T.A. and Kalayeh, H., J. Med. Chem., 34 (1991) 2824.Google Scholar
  15. 15.
    Bolis, G., Pace, L.D. and Fabrocini, F., J. Comput.-Aided Mol. Design, 5 (1991) 617.Google Scholar
  16. 16.
    Koile, K., Shapiro, R., Abarbanel, R.M., Webster, T.A., Lathrop, R.H. and Critchlow, R.E., In Noordewier, M., Searls, D. and Hunter, L. (Eds.) AAAI Workshop: AI Approaches to Classification and Pattern Recognition in Molecular Biology, AAAI, Anaheim, 1991, pp. 119–125.Google Scholar
  17. 17.
    King, R.D., Muggleton, S., Lewis, R.A. and Sternberg, M.J.E., Proc. Natl. Acad. Sci. USA, 89 (1992) 11322.Google Scholar
  18. 18.
    King, R.D., Muggleton, S., Lewis, R.A., Srinivasan, A., Feng, C. and Sternberg, M.J.E., In Mudge, T.N., Milutinovic, V. and Hunter, L. (Eds.) Proceedings of the 26th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press, Los Alimitos, 1993, pp. 646–655.Google Scholar
  19. 19.
    Simpson, P.F., Artificial Neural Systems, Pergamon Press, Oxford, 1990.Google Scholar
  20. 20.
    Rumelhart, D.E., Hinton, G.E. and Williams, R.J., Nature, 323 (1986) 533.Google Scholar
  21. 21.
    Gear, C.W., Numerical Initial Value Problems in Ordinary Differential Equations, Prentice Hall, Englewood Cliffs, 1971.Google Scholar
  22. 22.
    Muggleton, S. and Feng, C., In Akikawa, S., Goto, S., Ohsusa, S. and Yokomoki, T. (Eds.) Proceedings of the First Conference on Algorithmic Learning Theory, Jpn. Soc. Artif. Intel., Tokyo, 1990, pp. 368–381.Google Scholar
  23. 23.
    Muggleton, S., New Gen. Comput., 8 (1991) 295.Google Scholar
  24. 24.
    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 Diego, CA, 1992, pp. 339–347.Google Scholar
  25. 25.
    Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Med. Chem., submitted.Google Scholar
  26. 26.
    Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Med. Chem., submitted.Google Scholar
  27. 27.
    Roth, B., Aig, E., Rauckman, B.S., Srelitz, J., Phillips, A.P., Ferone, R., Bushby, S.R.M. and Siegel, C.W., J. Med. Chem., 24 (1981) 933.Google Scholar
  28. 28.
    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
  29. 29.
    Kendall, M. and Stuart, A., The Advanced Theory of Statistics, Griffen, London, 1977.Google Scholar
  30. 30.
    Ripley, B.D., Statistical Aspects of Neural Networks, In Proceedings of SemStat, Sandbjerg, Denmark, Chapman and Hall, London, 1992.Google Scholar
  31. 31.
    King, R., Henery, R., Feng, C. and Sutherland, A., A Comparative Study of Classification Algorithms: Statistical, Machine Learning, and Neural Network, In Michie, D., Muggleton, S. and Furukawa, F. (Eds.) Machine Intelligence and Inductive Learning, Vol. 13, Oxford University Press, Oxford, in press.Google Scholar

Copyright information

© ESCOM Science Publishers B.V. 1993

Authors and Affiliations

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

Personalised recommendations