Multiple molecular superpositioning as an effective tool for virtual database screening

Abstract

Molecular superpositioning is an important task in rational drug design. Usually it is the key step in a comparative analysis of molecules by 3D QSAR methods. Also it is helpful for the elucidation ofa pharmacophore and crucial in the attempt to derive a receptor model. Generally speaking, molecular superpositioning can be seen as the analog of molecular docking if the receptor structure is not available, and direct methods are not applicable. Virtual database screening is the computational counterpart to modern experimental techniques like high throughput screening and assaying ofcombinatorial libraries. Both screening techniques have the commongoal to detect active molecules in a large selection of compounds. Usually hundreds of thousands of candidates are to be tested, hence, time is the limiting factor and rapid processing of utmost importance. Descriptor-based methods that usually provide a simple linear encoding of the molecules meet the demands of computational speed and have been used predominantly for the task of virtual screening, for a long time. However, more powerful superposition methods have been developed during the past few years and now begin also to be applicable to screening large databases. Especially incombination with the faster methods, molecular superpositioning as the final step of a filtering protocol provides a powerful tool for virtual database screening. The present work reports on our latest developments of molecular superpositioning techniques and assessing their applicability to virtual database screening.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    Lemmen, C., Hiller, C. and Lengauer, T.: J. Computer-Aided Mol. Design, 12 (1998) 491.

    CAS  Article  Google Scholar 

  2. 2.

    Mestres, J., Rohrer, D.C. and Maggiora, G.M., J. Mol. Graph. Model., 15 (1997) 114.

    PubMed  CAS  Article  Google Scholar 

  3. 3.

    Crippen, G.M., J. Med. Chem., 22 (1979) 988.

    PubMed  CAS  Article  Google Scholar 

  4. 4.

    Crippen, G.M. and Havel, T.F., Distance Geometry and Molecular Conformation, Research Studies Press, Taunton, U.K., 1988.

    Google Scholar 

  5. 5.

    Marshall, G.R., Barry, C.D., Bosshard, H.D., Dammkoehler, R.D. and Dunn, D.A., In Olson, E.C. and Christoffersen, R.E. (Eds.), Computer-Assisted Drug Design, Vol. 112, American Chemical Society, Washington, DC, U.S.A., 1979, pp. 205–222.

    Google Scholar 

  6. 6.

    Takahashi, Y., Maeda, S. and Sasaki, S.-I., Anals Chimica Acta, 200 (1987) 363.

    CAS  Article  Google Scholar 

  7. 7.

    Martin, Y.C., Bures, M.G., Danaher, E.A., DeLazzer, J., Lico, I. and Pavlik, P.A., J. Comput.-Aided Mol. Design, 7 (1992) 83.

    Article  Google Scholar 

  8. 8.

    Lamdan, Y. and Wolfson, H.J., In IEEE International Conference on Computer Vision, Tampa, FL, 1988, pp. 238–249.

  9. 9.

    Nussinov, R. and Wolfson, H.J., Proc. Natl. Acad. Sci. USA, 88 (1991) 10495.

    PubMed  CAS  Article  Google Scholar 

  10. 10.

    Kabsch, W., Acta Crystallogr., A32 (1976) 922.

    Google Scholar 

  11. 11.

    Hurst, T., J. Chem. Inf. Comput. Sci., 34 (1994) 190.

    CAS  Article  Google Scholar 

  12. 12.

    Carbó, R., Leyda, L. and Arnau, M., Int. J. Quant. Chem., 17 (1980) 1185.

    Article  Google Scholar 

  13. 13.

    Hodgkin, E.E. and Richards, G., Int. J. Quant. Chem., Quant. Biol. Symp., 14 (1987) 105.

  14. 14.

    Thorner, D.A., Wild, D.J., Willett, P. and Wright, P.M., J. Chem. Inf. Comput. Sci., 36 (1996) 900.

    CAS  Article  Google Scholar 

  15. 15.

    Lemmen, C., Lengauer, T. and Klebe, G., J. Med. Chem., 41 (1998) 4502.

    PubMed  CAS  Article  Google Scholar 

  16. 16.

    Hahn, M., J. Chem. Inf. Comput. Sci., 37 (1997) 80.

    CAS  Article  Google Scholar 

  17. 17.

    Wang, T. and Zhou, J., J. Chem. Inf. Comput. Sci., 38 (1998) 71.

    PubMed  CAS  Article  Google Scholar 

  18. 18.

    Brint, A.T. and Willett, P., J. Chem. Inf. Comput. Sci., 27 (1997) 152.

    Article  Google Scholar 

  19. 19.

    Martin, Y.C., Bures, M.G. and Willett, P., In Lipkowitz, B. and Boyd, D.B. (Eds.), Reviews in Computational Chemistry, VCH, Weinheim, Germany, 1990, pp. 265–294.

    Google Scholar 

  20. 20.

    Johnson, M.A. and Maggiora, G.M. (Eds.), Concepts and Applications of Molecular Similarity, John Wiley & Sons, New York, NY, U.S.A., 1990.

    Google Scholar 

  21. 21.

    Kubinyi, H. (Ed.), 3D QSAR in Drug Design. Theory, Methods and Applications, ESCOM, Leiden, The Netherlands, 1993.

    Google Scholar 

  22. 22.

    Dean, P.M., In Dean, P.M. (Ed.), Molecular Similarity in Drug Design, Blackie Academic & Professional, London, U.K., 1995, pp. 1–23.

    Google Scholar 

  23. 23.

    Good, A.C., In Dean, P.M. (Ed.), Molecular Similarity in Drug Design, Blackie Academic & Professional, London, U.K., 1995, pp. 24–56.

    Google Scholar 

  24. 24.

    Humblet, C. and Dunbar Jr., J.B., In Venuti, M.C. (Ed.), Annual Reports in Medicinal Chemistry, Vol. 28, Chapter VI: Topics in Drug Design and Discovery, Academic Press, London, U.K., 1993, pp. 275–284.

    Google Scholar 

  25. 25.

    Klebe, G., In Kubinyi, H. (Ed.), 3D QSAR in Drug Design. Theory, Methods and Applications, ESCOM, Leiden, The Netherlands, 1993, pp. 173–199.

    Google Scholar 

  26. 26.

    Willett, P., J. Mol. Recogn., 8 (1995) 290.

    CAS  Article  Google Scholar 

  27. 27.

    Brown, R.D. and Martin, Y.C., J. Chem. Inf. Comput. Sci., 36 (1996) 572.

    CAS  Article  Google Scholar 

  28. 28.

    Bures, M.G., In Charifson, P.S. (Ed.), Practical Application of Computer-aided Drug Design, Marcel Dekker, New York, NY, U.S.A., 1997, pp. 39–72.

    Google Scholar 

  29. 29.

    Matter, H. and Rarey, M., In Jung, G. (Ed.), Combinatorial Organic Chemistry, Wiley-VCH, Weinheim, 2000.

    Google Scholar 

  30. 30.

    Lemmen, C. and Lengauer, T., J. Comput.-Aided Mol. Design, 14 (2000) 215.

    CAS  Article  Google Scholar 

  31. 31.

    Klebe, G., Mietzner, T. and Weber, F., J. Comput.-Aided Mol. Design, 8 (1994) 751.

    CAS  Article  Google Scholar 

  32. 32.

    Nissink, J.W.M., Verdonk, M.L., Kroon, J., Mietzner, T. and Klebe, G., J. Comput. Chem., 18 (1997) 638.

    CAS  Article  Google Scholar 

  33. 33.

    Lemmen, C. and Lengauer, T., J. Comput.-Aided Mol. Design, 11 (1997) 357.

    CAS  Article  Google Scholar 

  34. 34.

    Lemmen, C., Computational Methods for the Structural Alignment of Molecules. Number 1 in GMD Research Series. GMD - Forschungszentrum Informationstechnik, Sankt Augustin, Germany, 1999.

    Google Scholar 

  35. 35.

    Jones, G., Willett, P., Glen, R.C. and Taylor, R., J. Mol. Biol., 267 (1997) 727.

    PubMed  CAS  Article  Google Scholar 

  36. 36.

    Kramer, B., Rarey, M. and Lengauer, T., Proteins Struct. Funct. Genet., 37 (1999) 1.

    Google Scholar 

  37. 37.

    DAYLIGHT Inc., Mission Viejo, CA, U.S.A. DAYLIGHT Software Manual, 1994.

    Google Scholar 

  38. 38.

    Briem, H. and Kuntz, I.D., J. Med. Chem., 39 (1996) 3401.

    PubMed  CAS  Article  Google Scholar 

  39. 39.

    MDL Information Systems Inc., San Leandro, CA, U.S.A. MACCS Drug Data Report (MDDR).

  40. 40.

    NCI DB release (http://dtp.nci.nih.gov/docs/3d_database/structural_information/-structural_data.html, containing 126,710 structures, 07/01/1998. After conversion to SYBYL's mol2 format and validation 121,491 structures remained.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Christian Lemmen.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lemmen, C., Zimmermann, M. & Lengauer, T. Multiple molecular superpositioning as an effective tool for virtual database screening. Perspectives in Drug Discovery and Design 20, 43–62 (2000). https://doi.org/10.1023/A:1008712519162

Download citation

  • database filtering
  • molecular superpositioning
  • structural alignment
  • virtual screening