Journal of Computer-Aided Molecular Design

, Volume 12, Issue 5, pp 471–490 | Cite as

Feature trees: A new molecular similarity measure based on tree matching

  • Matthias Rarey
  • J. Scott Dixon

Abstract

In this paper we present a new method for evaluating molecular similarity between small organic compounds. Instead of a linear representation like fingerprints, a more complex description, a feature tree, is calculated for a molecule. A feature tree represents hydrophobic fragments and functional groups of the molecule and the way these groups are linked together. Each node in the tree is labeled with a set of features representing chemical properties of the part of the molecule corresponding to the node. The comparison of feature trees is based on matching subtrees of two feature trees onto each other. Two algorithms for tackling the matching problem are described throughout this paper. On a dataset of about 1000 molecules, we demonstrate the ability of our approach to identify molecules belonging to the same class of inhibitors. With a second dataset of 58 molecules with known binding modes taken from the Brookhaven Protein Data Bank, we show that the matchings produced by our algorithms are compatible with the relative orientation of the molecules in the active site in 61% of the test cases. The average computation time for a pair comparison is about 50 ms on a current workstation.

database screening molecular descriptors molecular similarity molecular superposition structural alignment 

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References

  1. 1.
    Willett, P., J. Mol. Recog., 8 (1995) 290.Google Scholar
  2. 2.
    Warr, W.A., J. Chem. Inf. Comput. Sci., 37 (1997) 134.Google Scholar
  3. 3.
    MDL Information Systems Inc., San Leandro, CA, USA. MACCS II.Google Scholar
  4. 4.
    DAYLIGHT Inc., Mission Viejo, California, USA. DAYLIGHT Software Manual, (1994).Google Scholar
  5. 5.
    Nilakantan, R., Bauman, N. and Venkataraghavan, R., J. Chem. Inf. Comput. Sci., 33 (1993) 79.Google Scholar
  6. 6.
    Sheridan, R.P., Miller, M.D., Underwood, D.J. and Kearsley, S.K., J. Chem. Inf. Comput. Sci., 36 (1996) 128.Google Scholar
  7. 7.
    Bemis, G.W. and Kuntz, I.D., J. Comput.-Aided Mol. Design, 6 (1992) 607.Google Scholar
  8. 8.
    Bath, P.A., Poirrette, A.R. and Willett, P., J. Chem. Inf. Comput. Sci., 34 (1994) 141.Google Scholar
  9. 9.
    Good, A.C., Ewing, T.J.A., Gschwend, D.A. and Kuntz, I.D., J. Comput.-Aided Mol. Design, 9 (1995) 1.Google Scholar
  10. 10.
    Briem, H. and Kuntz, I.D., J. Med. Chem., 39 (1996) 3401.Google Scholar
  11. 11.
    Brown, R.D. and Martin, Y.C., J. Chem. Inf. Comput. Sci., 36 (1996) 572.Google Scholar
  12. 12.
    Kearsley, S.K. and Smith, G.M., Tetrahedron Comput. Method., 3 (1990) 6C 615.Google Scholar
  13. 13.
    Klebe, G., Mietzner, T. and Weber, F., J. Comput.-Aided Mol. Design, 8 (1994) 751.Google Scholar
  14. 14.
    Jones, G., Willett, P. and Glen, R.C., J. Comput.-Aided Mol. Design, 9 (1995) 532.Google Scholar
  15. 15.
    Lemmen, C. and Lengauer, T., J. Comput.-Aided Mol. Design, 11 (1997) 357.Google Scholar
  16. 16.
    Kubinyi, H. (Ed.) 3D QSAR in Drug Design. Theory, Methods and Applications, ESCOM, Leiden, (1993).Google Scholar
  17. 17.
    Gillet, V.J., Downs, G.M., Holliday, J.D., Lynch, M.F. and Dethlefsen, W., J. Chem. Inf. Comput. Sci., 31 (1991) 260.Google Scholar
  18. 18.
    MDL Information Systems Inc., San Leandro, CA, USA. MACCS Drug Data Report (MDDR).Google Scholar
  19. 19.
    Bernstein, F.C., Koetzle, T.F., Williams, G.J.B., Meyer, E.F. Jr., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T. and Tasumi, M., J. Mol. Biol., 112 (1977) 535.Google Scholar
  20. 20.
    Corman, T.H., Leiserson, C.E. and Rivest, R.L., Introduction to Algorithms, MIT Press, Cambridge, MA (1990).Google Scholar
  21. 21.
    Goede, A., Preissner, R. and Frömmel, C., J. Comput. Chem., 18 (1997) 1113.Google Scholar
  22. 22.
    Rarey, M., Kramer, B., Lengauer, T. and Klebe, G., J. Mol. Biol., 261 (1996) 3 470.Google Scholar
  23. 23.
    Mattos, C. and Ringe, D., In Kubinyi, H. (Ed.), 3D QSAR in Drug Design. Theory, Methods and Applications, ESCOM, Leiden, 1993, pp. 226–254.Google Scholar
  24. 24.
    Kabsch, W., Acta Crystallogr., A32 (1976) 922.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Matthias Rarey
    • 1
  • J. Scott Dixon
    • 2
  1. 1.German National Research Center for Information Technology (GMD), Institute for Algorithms and Scientific Computing (SCAI)Schloß BirlinghovenSankt AugustinGermany
  2. 2.SmithKline Beecham Pharmaceuticals, Physical and Structural ChemistryKing of PrussiaU.S.A

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