Journal of Computer-Aided Molecular Design

, Volume 23, Issue 3, pp 143–152 | Cite as

An improved scoring function for suboptimal polar ligand complexes

  • Giovanni Cincilla
  • David Vidal
  • Miquel PonsEmail author


Learning strategies can be used to improve the efficiency of virtual screening of very large databases. In these strategies new compounds to be screened are selected on the basis of the results obtained in previous stages, even if truly good ligands have not yet been identified. This approach requires that the scoring function used correctly predicts the energy and geometry of suboptimal complexes, i.e. weak complexes that are not the final solution of the screening but help direct the search toward the most productive regions of chemical space. We show that a small modification in the treatment of the solvation of polar atoms corrects the tendency of the original Autodock 3.0 scoring function to bury ligand polar atoms away from solvent, even if no complementary groups are present in the target and improves the performance of Autodock 3.0 and 4.0 in reproducing the experimental docking energies of weak complexes, resembling the suboptimal complexes encountered in the intermediate stages of virtual screening.


Docking Drug design Virtual screening Scoring function Solvation 



This work has been supported by funds from the Spanish Ministerio de Educación y Ciencia-FEDER BIO2004-5436 and BIO2007-63458 and the Generalitat de Catalunya. DV and GC gratefully acknowledge predoctoral grants from the Spanish Ministerio de Educación y Ciencia.

Supplementary material

10822_2008_9246_MOESM1_ESM.doc (392 kb)
(DOC 392 kb)


  1. 1.
    Walters WP, Sthal MT, Murcko MA (1998) Drug Discov Today 3:160. doi: 10.1016/S1359-6446(97)01163-X CrossRefGoogle Scholar
  2. 2.
    Seifert MHJ, Wolf K, Vitt D (2003) BIOSILICO 1:143. doi: 10.1016/S1478-5382(03)02359-X CrossRefGoogle Scholar
  3. 3.
    Lengauer T, Lemmen C, Rarey M, Zimmermann M (2004) Drug Discov Today 9:27. doi: 10.1016/S1359-6446(04)02939-3 CrossRefGoogle Scholar
  4. 4.
    Mestres J (2002) Biochem Soc Trans 30:797. doi: 10.1042/BST0300797 CrossRefGoogle Scholar
  5. 5.
    Jones G (1998) Encyclopedia of computational chemistry: genetic and evolutionary algorithms. Wiley Publisher, ChichesterGoogle Scholar
  6. 6.
    Vidal D, Thormann M, Pons M (2006) J Chem Inf Model 46:836. doi: 10.1021/ci050458q CrossRefGoogle Scholar
  7. 7.
    Barbosa F, Horvath D (2004) Curr Top Med Chem 4:589. doi: 10.2174/1568026043451186 CrossRefGoogle Scholar
  8. 8.
    Shanmugasundaram V, Maggiora GM, Lajiness MS (2005) J Med Chem 48:240. doi: 10.1021/jm0493515 CrossRefGoogle Scholar
  9. 9.
    Warmuth MK, Liao J, Rätsch G, Mathieson M, Putta S, Lemmen C (2003) J Chem Inf Comput Sci 43:667. doi: 10.1021/ci025620t Google Scholar
  10. 10.
    Morris GM, Goodsell DS, Halliday SR, Huey R, Hart WE, Belew RK et al (1998) J Comput Chem 19:1639. doi:10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-BCrossRefGoogle Scholar
  11. 11.
    Sousa SF, Fernandes PA, Ramos MJ (2006) Proteins 65:15. doi: 10.1002/prot.21082 CrossRefGoogle Scholar
  12. 12.
    Vidal D, Thormann M, Pons M (2005) J Chem Inf Model 45:386. doi: 10.1021/ci0496797 CrossRefGoogle Scholar
  13. 13.
    Thormann M, Pons M (2001) J Comput Chem 22:1971. doi: 10.1002/jcc.1146 CrossRefGoogle Scholar
  14. 14.
    Hetényi C, Paragi G, Maran U, Timár Z, Karelson M, Penke B (2006) J Am Chem Soc 128:1233. doi: 10.1021/ja055804z CrossRefGoogle Scholar
  15. 15.
    Huey R, Morris GM, Olson AJ, Goodsell DS (2007) J Comput Chem 28:1145. doi: 10.1002/jcc.20634 CrossRefGoogle Scholar
  16. 16.
    Stouten PFW, Frömmel C, Nakamura H, Sander C (1993) Mol Simul 10:97. doi: 10.1080/08927029308022161 CrossRefGoogle Scholar
  17. 17.
    Block P, Sotriffer CA, Dramburg I, Klebe G (2006) Nucleic Acids Res 34:D522. doi: 10.1093/nar/gkj039 CrossRefGoogle Scholar
  18. 18.
    Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) Nucleic Acids Res 35:D198. doi: 10.1093/nar/gkl999 CrossRefGoogle Scholar
  19. 19.
    AffinDB is freely accessible at Accessed Jan 2005
  20. 20.
    Barbault F, Zhang L, Fan BT (2006) Chemom Intell Lab Syst 82:269. doi: 10.1016/j.chemolab.2005.05.014 CrossRefGoogle Scholar
  21. 21.
    ChemAxon Ltd Budapest, Hungary. Accessed Jan 2005
  22. 22.
    Gerber PR MOLOC—a molecular design software suite. Accessed Jan 2005

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Giovanni Cincilla
    • 1
    • 2
  • David Vidal
    • 1
    • 2
    • 3
  • Miquel Pons
    • 1
    • 2
    Email author
  1. 1.Laboratory of Biomolecular NMRInstitute for Research in Biomedicine, Science Research ParkBarcelonaSpain
  2. 2.Departament de Química OrgànicaUniversitat de BarcelonaBarcelonaSpain
  3. 3.CHEMOTARGETS S.L.BarcelonaSpain

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