Journal of Computer-Aided Molecular Design

, Volume 16, Issue 1, pp 59–71 | Cite as

Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases

  • John W. Raymond
  • Peter Willett

Abstract

This paper reports an evaluation of both graph-based and fingerprint-based measures of structural similarity, when used for virtual screening of sets of 2D molecules drawn from the MDDR and ID Alert databases. The graph-based measures employ a new maximum common edge subgraph isomorphism algorithm, called RASCAL, with several similarity coefficients described previously for quantifying the similarity between pairs of graphs. The effectiveness of these graph-based searches is compared with that resulting from similarity searches using BCI, Daylight and Unity 2D fingerprints. Our results suggest that graph-based approaches provide an effective complement to existing fingerprint-based approaches to virtual screening.

fingerprint graph matching maximum common edge subgraph maximum overlapping set RASCAL similarity coefficient similarity searching virtual screening 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Klebe, G. (ed.), Perspect. Drug Discov. Design, 20 (2000) 1.Google Scholar
  2. 2.
    Bohm, H. J. and Schneider, G. (Eds.), Virtual Screening for Bioactive Molecules, Wiley-VCH, Weinheim, 2000.Google Scholar
  3. 3.
    Willett, P., Barnard, J.M. and Downs, G.M., ]J. Chem. Inf. Comput. Sci., 38 (1998) 983.Google Scholar
  4. 4.
    Johnson, M. and Maggiora, G. (eds), Concepts and Applications of Molecular Similarity, John Wiley, New York, 1990.Google Scholar
  5. 5.
    Dean, P.M. (Ed.), Molecular Similarity in Drug Design, Chapman and Hall, Glasgow, 1994.Google Scholar
  6. 6.
    Brown, R.D. and Martin, Y.C., J. Chem. Inf. Comput. Sci., 36 (1996) 572.Google Scholar
  7. 7.
    Patterson, D.E., Cramer, R.D., Ferguson, A.M., Clark, R.D. and Weinberger, L.E., J. Med. Chem., 39 (1996) 3049.Google Scholar
  8. 8.
    Raymond, J., Gardiner, E.J. and Willett, P., J. Chem. Inf. Comput. Sci., 42 (2002) 375.Google Scholar
  9. 9.
    Raymond, J., Gardiner, E.J. and Willett, P., Comput. J., in press.Google Scholar
  10. 10.
    Sanfeliu, A. and Fu, K. S., IEEE Trans. Syst. Man Cybern., 13 (1983) 353.Google Scholar
  11. 11.
    Barnard Chemical Information Ltd. is at URL www.bci1.demon.co.ukGoogle Scholar
  12. 12.
    Daylight Chemical Information Systems Inc. is at URL www.daylight.comGoogle Scholar
  13. 13.
    Tripos Inc. is at URL www.tripos.comGoogle Scholar
  14. 14.
    Ellis, D., Furner-Hines, J. and Willett, P., Perspect. Inf. Manag., 3 (1993) 128.Google Scholar
  15. 15.
    Tulloss, R. E., In Palm, M.E. and Chapela, I.H. (eds), Mycology in Sustainable Development: Expanding Concepts, Parkway Publishers, Boone, North Carolina, 1997, 122.Google Scholar
  16. 16.
    Hayek, L., In Heyer, W. (Eds.), Measuring and Monitoring Miological Diversity: Standard Methods for Amphibians, Smithsonian Institution, Washington, D. C., 1994, 207.Google Scholar
  17. 17.
    Goddard, W. and Swart, H.C., Discrete Math., 161 (1996) 121.Google Scholar
  18. 18.
    Chartrand, G., Saba, F. and Zou, H., Cas. Pest. Mat., 110 (1985) 87.Google Scholar
  19. 19.
    Bunke, H., Pattern Recogn. Lett., 18 (1997) 689.Google Scholar
  20. 20.
    Skvortsova, M.I., Baskin, I.I., Stankevich, I.V., Palyulin, V.A. and Zefirov, N.S., J. Chem. Inf. Comput. Sci., 38 (1998) 785.Google Scholar
  21. 21.
    Wallis, W.D., Shoubridge, P., Kraetz, M. and Ray, D., Pattern Recogn. Lett., 22 (2001) 701.Google Scholar
  22. 22.
    Johnson, M., Naim, M., Nicholson, V. and Tsai, C., In King, R.B. and Rouvray, D.H. (eds), Graph Theory and Topology in Chemistry, Elsevier Science Publishers, 1987, 219.Google Scholar
  23. 23.
    Johnson, M., In Alavi, Y., Chartrand, G., Lesniak, L., Lick, D. and Wall, C. (eds), Graph Theory and Its Applications to Algorithms and Computer Science, John Wiley, New York, 1985, p. 457.Google Scholar
  24. 24.
    Bunke, H. and Shearer, K., Pattern Recogn. Lett., 19 (1998) 255.Google Scholar
  25. 25.
    Sheridan, R.P. and Miller, M.D., J. Chem. Inf. Comput. Sci., 38 (1998) 915.Google Scholar
  26. 26.
    Edgar, S.J., Holliday, J.D. and Willett, P., J. Mol. Graph. Model., 18 (2000) 343.Google Scholar
  27. 27.
    Guner, O.F. at http://www.netsci.org/Science/Cheminform/ feature09.htmlGoogle Scholar
  28. 28.
    Guner, O.F., In Guner, O.F. and Henry, D.R., (Eds.), Pharmacophore Perception, Development, and Use in Drug Design, International University Line, La Jolla, CA, USA, 2000, 194.Google Scholar
  29. 29.
    Holliday, J. D., Hu, C. Y. and Willett, P., Combin. Chem. High Throughput Screen, 5 (2002) 155.Google Scholar
  30. 30.
    Tzitzikas, Y., IEEE International Conference on Computer Systems and Applications, Beirut, Lebanon, 2001.Google Scholar
  31. 31.
    Downs, G. M. and Willett, P., Rev. Comput. Chem., 7 (1995) 1Google Scholar
  32. 32.
    Flower, D. R., J. Chem. Inf. Comput. Sci..38 (1998) 379.Google Scholar
  33. 33.
    Godden, J.W., Xue, L. and Bajorath, J., J. Chem. Inf. Comput. Sci., 40 (2000) 163.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • John W. Raymond
    • 1
  • Peter Willett
    • 2
  1. 1.Ann Arbor LaboratoriesPfizer Global Research and DevelopmentAnn ArborU.S.A
  2. 2.Krebs Institute for Biomolecular Research and Department of Information StudiesUniversity of SheffieldWestern Bank, SheffieldU.K

Personalised recommendations