Journal of Computer-Aided Molecular Design

, Volume 16, Issue 11, pp 825–830 | Cite as

Development of biologically active compounds by combining 3D QSAR and structure-based design methods

  • Wolfgang Sippl


One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved – namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It is expected that concepts from receptor-based 3D QSAR will be valuable tools for the analysis of high-throughput screening as well as virtual screening data

AutoDock binding affinity prediction CoMFA docking GRID GOLPE 3D QSAR. 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cramer, R.D., III, Patterson, D.E. and Bunce, J.D., J. Am. Chem. Soc., 110 (1988) 5959.Google Scholar
  2. 2.
    Klebe, G. and Abraham, U., J. Med. Chem., 36 (1993) 70.PubMedGoogle Scholar
  3. 3.
    Taylor, R.D., Jewsbury, P.J., Essex, J.W., J. Comput. Aided Mol. Des., 16 (2002) 151.PubMedGoogle Scholar
  4. 4.
    Tame, J.R.H., J. Comput. Aided Mol. Des., 13 (1999) 99. _PubMedGoogle Scholar
  5. 5.
    Kollman, P., Chem. Rev., 93 (1993) 2395.Google Scholar
  6. 6.
    Ortiz, A.R., Pisabarro, M.T., Gago, F. and Wade, R.C., J.Med. Chem., 38 (1995) 2681.Google Scholar
  7. 7.
    Holloway, M.K., Wai, J.M., Halgren, T.A., Fitzgerald, P.M., Vacca, J.P., Dorsey, B.D., Levin, R.B., Thompson W.J., Chen, L.J. and deSolms, S.J., J. Med. Chem., 38 (1995) 2280.Google Scholar
  8. 8.
    Cho, S.J, Garsia, M.L., Bier, J. and Tropsha, A., J. Med. Chem., 39 (1996) 5064.Google Scholar
  9. 9.
    Tokarski, J.S. and Hopfinger, A. J., J. Chem. Inf. Comput. Sci., 4 (1997) 792.Google Scholar
  10. 10.
    Sippl, W., Contreras, J. M., Rival, Y. and Wermuth, C.G., In: K. Gundertofte, F.S: Jorgensen (Eds.), Molecular Modelling and Predicting of Bioactivity, Plenum Press, New York, pp. 53–58 (1998).Google Scholar
  11. 11.
    Vaz, R.J., McLEan, L.R. and Pelton, J.T., J. Comput. Aided Mol. Des., 12 (1998) 99.PubMedGoogle Scholar
  12. 12.
    Bursi, R. and Grootenhuis, P.D. J. Comput. Aided Mol. Des., 12 (1999) 341.Google Scholar
  13. 13.
    Lozano, J.J., Pastor, M., Cruciani, G., Gaedt, K., Centeno, N.B., Gago, F. and Sanz, F., J. Comput. Aided Mol. Des., 13 (2000) 341.Google Scholar
  14. 14.
    Sippl, W., Contreras, J. M., Parrot, I., Rival, Y. and Wermuth, C.G., J. Comput. Aided Mol. Des. 15, (2001) 395.PubMedGoogle Scholar
  15. 15.
    Sippl, W., Bioorg. Med. Chem., 10 (2002) 3741.Google Scholar
  16. 16.
    Vieth, M. and Cummins, D.J. J. Med. Chem., 43 (2000) 3020.Google Scholar
  17. 17.
    Costantino, G., Macchiarulo, A., Camaioni, E. and Pellicciari, R., J. Med. Chem., 44 (2001) 3786.Google Scholar
  18. 18.
    Contreras, J. M., Parrot, I., Sippl, W., Rival, Y. M. and Wermuth, C.G., J. Med. Chem., 44 (2001) 2707.Google Scholar
  19. 19.
    Malamas, M.S., Sredy, J., Moxham, C., Katz, A., Xu, W., McDevitt, R., Adebayo, F.O., Sawicki, D.R., Seestaller, L., Sullivan, D., Taylor J.R., J. Med. Chem., 43 (2000) 1293.Google Scholar
  20. 20.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., and Bourne, P.E., Nucleic Acids Research, 28 (2000) 235.Google Scholar
  21. 21.
    Singh, U. C. and Kollman, P. A., J. Comput. Chem., 5 (1984) 129.Google Scholar
  22. 22.
    Goodsell, D. S., Morris G. M. and Olson, A. J., J. Mol. Recognit., 9 (1996) 1.PubMedGoogle Scholar
  23. 23.
    Vedani, A. and Huhta, D. W., J. Am. Chem. Soc., 112 (1990) 269.Google Scholar
  24. 24.
    Baroni, M., Constantino, G., Cruciani, G., Riganelli, D, Valigi, R. and Clementi, S., Quant. Struct.-Act. Relat., 12 (1993) 9.Google Scholar
  25. 25.
    Pastor, M., Cruciani, G. and Watson, K., J. Med. Chem., 40 (1997) 4089. 830Google Scholar
  26. 26.
    Sippl, W., J. Comput. Aided Mol. Des., 14 (2000) 559.PubMedGoogle Scholar
  27. 27.
    Liu, H., Huang, X., Shen, J., Luo, X., Li, M., Xiong, B., Chen, G., Shen, J., Yang, Y., Jiang, H. and Chen, K., J. Med. Chem., 45 (2002) 4816.Google Scholar
  28. 28.
    Huang, X., Xu, L., Luo, X., Fan, K., Ji, R., Pei, G., Chen, K. and Jiang, H. J. Med. Chem., 45 (2002) 333.PubMedGoogle Scholar
  29. 29.
    Gohlke, H. and Klebe, G., J. Med. Chem., 45 (2002) 4153.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Wolfgang Sippl
    • 1
  1. 1.Institute for Pharmaceutical ChemistryHeinrich-Heine-Universität DüsseldorfDüsseldorfGermany

Personalised recommendations