Journal of Computer-Aided Molecular Design

, Volume 15, Issue 2, pp 173–181 | Cite as

Warmr: a data mining tool for chemical data

  • Ross D. King
  • Ashwin Srinivasan
  • Luc Dehaspe


Data mining techniques are becoming increasingly important in chemistry as databases become too large to examine manually. Data mining methods from the field of Inductive Logic Programming (ILP) have potential advantages for structural chemical data. In this paper we present Warmr, the first ILP data mining algorithm to be applied to chemoinformatic data. We illustrate the value of Warmr by applying it to a well studied database of chemical compounds tested for carcinogenicity in rodents. Data mining was used to find all frequent substructures in the database, and knowledge of these frequent substructures is shown to add value to the database. One use of the frequent substructures was to convert them into probabilistic prediction rules relating compound description to carcinogenesis. These rules were found to be accurate on test data, and to give some insight into the relationship between structure and activity in carcinogenesis. The substructures were also used to prove that there existed no accurate rule, based purely on atom-bond substructure with less than seven conditions, that could predict carcinogenicity. This results put a lower bound on the complexity of the relationship between chemical structure and carcinogenicity. Only by using a data mining algorithm, and by doing a complete search, is it possible to prove such a result. Finally the frequent substructures were shown to add value by increasing the accuracy of statistical and machine learning programs that were trained to predict chemical carcinogenicity. We conclude that Warmr, and ILP data mining methods generally, are an important new tool for analysing chemical databases.

carcinogenesis chemical structure inductive logic programming machine learning predictive toxicology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy (Eds) Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, MA, 1996.Google Scholar
  2. 2.
    Communications of the ACM. Special issue on data mining 39, 11 (1996).Google Scholar
  3. 3.
    Agrawal, R., Imielinski, T. and Swami, A., in Buneman, P. and Jajodia, S. (Eds), Proceedings of the ACM SIGMOD Conference on Management of Data (1993) 207–216.Google Scholar
  4. 4.
    Mitchell, T.M. Machine Learning. McGraw-Hill, New York, NY, 1997.Google Scholar
  5. 5.
    Hansch, C., Malony, P.P., Fujiya, T. and Muir, R.M., Nature 194, (1962) 178.Google Scholar
  6. 6.
    Martin, Y.C. Quantitative Drug Design: A Critical Introduction, Marcel Dekker, New York, NY, 1978.Google Scholar
  7. 7.
    Klopman, G., J. Am. Chem. Soc., 106 (1984) 7315.Google Scholar
  8. 8.
    Cramer, R.D., Patterson, D.E. and Bunce, J.D., J. Am. Chem. Soc., 110 (1988) 5959.Google Scholar
  9. 9.
    Chen, X., Rusinko, A. and Young, S.S., J. Chem. Inf. Comput. Sci., 38 (1998) 1054.Google Scholar
  10. 10.
    Rusinko, A., Farmen, M.W., Lambert, C.G., Brown, P.L. and Young, S.S., J. Chem. Inf. Comput. Sci., 39 (1999) 1017.Google Scholar
  11. 11.
    Muggleton, S. (Ed.) Inductive Logic Programming. Academic Press, London, 1992.Google Scholar
  12. 12.
    Lavrac, N. and Dzeroski, S., Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, 1994.Google Scholar
  13. 13.
    King, R.D., Muggleton, S., Lewis R.A. and Sternberg, M.J.E., Proc. Natl. Acad. Sci. USA, 89 (1992) 11322.PubMedGoogle Scholar
  14. 14.
    Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comp. Aid. Mol. Des., 8 (1994) 405.Google Scholar
  15. 15.
    Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comp. Aid. Mol. Des., 8 (1994) 421.Google Scholar
  16. 16.
    King, R.D., Muggleton, S.H., Srinivasan, A. and Sternberg, M.J.E., Proc. Natl. Acad. Sci. USA, 93 (1996) 438.PubMedGoogle Scholar
  17. 17.
    King, R.D. & Srinivasan, A., Env. Health Perspect., 104 (supplement 5) (1996) 1031.Google Scholar
  18. 18.
    King, R.D. and Srinivasan, A., J. Comp. Aid. Mol. Des., 11 (1998) 571.Google Scholar
  19. 19.
    Finn, P., Muggleton S., Page, D. and Srinivasan, A., Machine Learning J., 30 (1998) 241.Google Scholar
  20. 20.
    Dehaspe, L. and De Raedt, L., Lecture Notes in Artificial Intelligence, vol. 1297. Springer-Verlag, New York, NY, 1997.Google Scholar
  21. 21.
    Dehaspe, L. and Toivonen. H., Data Mining Knowledge Discovery, 3 (1999) 7.Google Scholar
  22. 22.
    Huff, J. and Hasernan, J., Env. Health Perspect., 96 (1991) 23.Google Scholar
  23. 23.
    Ashby, J. and Tennant, R.W., Mutation Res., 257 (1991) 229.PubMedGoogle Scholar
  24. 24.
    Tennant, R.W., Spalding, J., Stasiewicz, S. and Ashby, J., Mutagenesis, 5 (1990) 3.Google Scholar
  25. 25.
    Bahler, D.R. and Bristol, D.W., in Hunter, L., Searls, D. and Shavlik, D. (Eds), Proceedings of the First International Conference on Intelligent Systems for Molecular Biology MIT Press, Menlo Park, 1993, pp. 29–37.Google Scholar
  26. 26.
    Bristol, D.W., Wachsman, J.T. and Greenwall, A., Env. Health Perspect., 104 (supplement 5) (1996) 1001.Google Scholar
  27. 27.
    Srinivasan, A., King, R.D., Bristol, D.W., in Dzeroski, S and Flach, P.A. (Eds), Proceedings of the Ninth International Workshop on Inductive Logic programming LNAI. Springer-Verlag Berlin, 1999, pp. 291–302.Google Scholar
  28. 28.
    Srinivasan, A., King, R.D., Muggleton, S.H. and Sternberg, M.J.E., Fifteenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, 1997, pp. 4–9.Google Scholar
  29. 29.
    Srinivasan, A., King, R.D., Bristol, D.W., Sixteenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, 1999, pp. 270–275.Google Scholar
  30. 30.
    Ullman, J.D., Principles of Database and Knowledge-Base Systems. MD Computer Science Press, Rockville, 1988.Google Scholar
  31. 31.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A.I., in Fayyad, U.M, Piatetsky-Shapiro, G., Smyth and Uthurusamy, R. (Eds), Advances in Knowledge Discovery and Data mining AAAI Press, Menlo Park, CA, 1996, pp. 307–328.Google Scholar
  32. 32.
    Mannila, H. and Toivonen, H., Data Mining and Knowledge Discovery, 1 (1997) 241.Google Scholar
  33. 33.
    Muggleton, S., New Gen. Comput., 13 (1995) 245.Google Scholar
  34. 34.
    Blockeel, H. and De Raedt, L. Artif. Intell., 101 (1998) 285.Google Scholar
  35. 35.
    Bahler, D. and Bristol, D., Predictive Toxicology of Chemicals: Experience and Impact of AI tools (AAAI Spring Symposium Technical Report SS–99-01) AAAI Press Menlo Park CA, 1999, pp. 74-77.Google Scholar
  36. 36.
    Quinlan, J.R., C4.5: Programs for Empirical Learning, Morgan Kaufmann, San Fancisco, CA, 1993.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Ross D. King
    • 1
  • Ashwin Srinivasan
    • 2
  • Luc Dehaspe
    • 3
  1. 1.Department of Computer ScienceUniversity of WalesWalesUK
  2. 2.Computing LaboratoryUniversity of OxfordOxfordUK
  3. 3.PharmaDMBelgium

Personalised recommendations