2D Pharmacophore Query Generation

  • David Hoksza
  • Petr Škoda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8492)

Abstract

Using pharmacophores in virtual screening of large chemical compound libraries proved to be a valuable concept in computer-aided drug design. Traditionally, pharmacophore-based screening is performed in 3D space where crystallized or predicted structures of ligands are superposed and where pharmacophore features are identified and compiled into a 3D pharmacophore model. However, in many cases the structures of the ligands are not known which results in using a 2D pharmacophore model.

We introduce a method capable of automatic generation of 2D pharmacophore models given previous knowledge about the biological target of interest. The knowledge comprises of a set of known active and inactive molecules with respect to the target. From the set of active and inactive molecules 2D pharmacophore features are extracted using pharmacophore fingerprints. Then a statistical procedure is applied to identify features separating the active from the inactive molecules and these features are then used to build a pharmacophore model. Finally, a similarity measure utilizing the model is applied for virtual screening.

The method was tested on multiple state of the art datasets and compared to several virtual screening methods. Our approach seems to exceed the existing methods in most cases. We believe that the presented methodology forms a valuable addition to the set of tools available for the early stage drug discovery process.

Keywords

2D pharmacophores pharmacophore modeling virtual screening 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    RDKit, http://www.rdkit.org/ (accessed: April 15, 2014)
  2. 2.
    Baumann, K.: An alignment-independent versatile structure descriptor for qsar and qspr based on the distribution of molecular features. Journal of Chemical Information and Computer Sciences 42(1), 26–35 (2002)MathSciNetGoogle Scholar
  3. 3.
    Cheng, T., Li, Q., Zhou, Z., Wang, Y., Bryant, S.H.: Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J. 14(1), 133–141 (2012)CrossRefGoogle Scholar
  4. 4.
    Ehrlich, P.: Über den jetzigen Stand der Chemotherapie. Verlag der Chemiker-Zeitung Otto v. Halem (1908)Google Scholar
  5. 5.
    Ferreira, R.S., Simeonov, A., Jadhav, A., Eidam, O., Mott, B.T., Keiser, M.J., McKerrow, J.H., Maloney, D.J., Irwin, J.J., Shoichet, B.K.: Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J. Med. Chem. 53(13), 4891–4905 (2010)CrossRefGoogle Scholar
  6. 6.
    Fisher, R.A.: On the Interpretation of 2 from Contingency Tables, and the Calculation of P. Journal of the Royal Statistical Society 85(1), 87–94 (1922)CrossRefGoogle Scholar
  7. 7.
    Gaulton, A., Bellis, L.J., Bento, A.P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., Overington, J.P.: ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research 40(D1), D1100–D1107 (2011)Google Scholar
  8. 8.
    Heikamp, K., Bajorath, J.: The future of virtual compound screening. Chem. Biol. Drug Des. 81(1), 33–40 (2013)CrossRefGoogle Scholar
  9. 9.
    Hert, J., Willett, P., Wilton, D.J., Acklin, P., Azzaoui, K., Jacoby, E., Schuffenhauer, A.: Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. Journal of Chemical Information and Computer Sciences 44(3), 1177–1185 (2004)Google Scholar
  10. 10.
    Hert, J., Willett, P., Wilton, D.J., Acklin, P., Azzaoui, K., Jacoby, E., Schuffenhauer, A.: Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Org. Biomol. Chem. 2, 3256–3266 (2004)CrossRefGoogle Scholar
  11. 11.
    Horvath, D.: Pharmacophore-based virtual screening. Methods Mol. Biol. 672, 261–298 (2011)CrossRefGoogle Scholar
  12. 12.
    Chemical Computing Group Inc.: Molecular Operating Environment (MOE), 2013.08 (2013)Google Scholar
  13. 13.
    Irwin, J.J., Sterling, T., Mysinger, M.M., Bolstad, E.S., Coleman, R.G.: ZINC: A Free Tool to Discover Chemistry for Biology. J. Chem. Inf. Model. 52(7), 1757–1768 (2012)CrossRefGoogle Scholar
  14. 14.
    Johnson, M.A., Maggiora, G.M.: Concepts and Applications of Molecular Similarity. Wiley-Interscience (1990)Google Scholar
  15. 15.
    Jorgensen, W.L.: Rusting of the lock and key model for protein-ligand binding. Science 254(5034), 954–955 (1991)CrossRefGoogle Scholar
  16. 16.
    Liao, C., Sitzmann, M., Pugliese, A., Nicklaus, M.C.: Software and resources for computational medicinal chemistry. Future Med. Chem. 3(8), 1057–1085 (2011)CrossRefGoogle Scholar
  17. 17.
    McGregor, M.J., Pallai, P.V.: Clustering of large databases of compounds: using the mdl keys as structural descriptors. Journal of Chemical Information and Computer Sciences 37(3), 443–448 (1997)Google Scholar
  18. 18.
    R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008) ISBN 3-900051-07-0Google Scholar
  19. 19.
    Ripphausen, P., Nisius, B., Bajorath, J.: State-of-the-art in ligand-based virtual screening. Drug Discov. Today 16(9-10), 372–376 (2011)CrossRefGoogle Scholar
  20. 20.
    Ripphausen, P., Nisius, B., Peltason, L., Bajorath, J.: Quo Vadis, Virtual Screening? A Comprehensive Survey of Prospective Applications. Journal of Medicinal Chemistry 53(24), 8461–8467 (2010)CrossRefGoogle Scholar
  21. 21.
    Ripphausen, P., Stumpfe, D., Bajorath, J.: Analysis of structure-based virtual screening studies and characterization of identified active compounds. Future Med. Chem. 4(5), 603–613 (2012)CrossRefGoogle Scholar
  22. 22.
    Rohrer, S.G., Baumann, K.: Maximum unbiased validation (muv) data sets for virtual screening based on pubchem bioactivity data. Journal of Chemical Information and Modeling 49(2), 169–184 (2009)CrossRefGoogle Scholar
  23. 23.
    Schneider, G., Neidhart, W., Giller, T., Schmid, G.: Scaffold-hopping by topological pharmacophore search: A contribution to virtual screening. Angewandte Chemie International Edition 38(19), 2894–2896 (1999)CrossRefGoogle Scholar
  24. 24.
    Sun, H.: Pharmacophore-Based Virtual Screening. Current Medicinal Chemistry 15(10), 1018–1024 (2008)CrossRefGoogle Scholar
  25. 25.
    Taboureau, O., Baell, J.B., Fernandez-Recio, J., Villoutreix, B.O.: Established and emerging trends in computational drug discovery in the structural genomics era. Chem. Biol. 19(1), 29–41 (2012)CrossRefGoogle Scholar
  26. 26.
    Tanimoto, T.: IBM Internal Report 17th November (1957)Google Scholar
  27. 27.
    Vidler, L.R., Filippakopoulos, P., Fedorov, O., Picaud, S., Martin, S., Tomsett, M., Woodward, H., Brown, N., Knapp, S., Hoelder, S.: Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening. J. Med. Chem. 56(20), 8073–8088 (2013)CrossRefGoogle Scholar
  28. 28.
    Wang, Y., Xiao, J., Suzek, T.O., Zhang, J., Wang, J., Bryant, S.H.: Pubchem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research 37(Web-Server-Issue), 623–633 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David Hoksza
    • 1
  • Petr Škoda
    • 1
  1. 1.FMP, Department of Software EngineeringCharles University in PraguePragueCzech Republic

Personalised recommendations