Journal of Computer-Aided Molecular Design

, Volume 24, Issue 10, pp 865–878

QMOD: physically meaningful QSAR

Article

Abstract

Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such methods have utility in retrospective rationalization of activity patterns of substituents on a common scaffold, but are limited when either multiple scaffolds are present or when ligand alignment varies significantly based on structural changes. In addition, such methods generally assume independence and additivity of effect from scaffold substituents. Collectively, these non-physical modeling assumptions sharply limit the utility of widely used QSAR approaches for prospective prediction of ligand activity. The recently introduced Surflex-QMOD approach, by virtue of constructing physical models of binding sites, comes closer to a modeling approach that is congruent with protein ligand binding events. A set of congeneric CDK2 inhibitors showed that induced binding pockets can be quite congruent with the enzyme’s active site but that model predictivity within a chemical series does not necessarily depend on congruence. Muscarinic antagonists were used to show that the QMOD approach is capable of making accurate predictions in cases where highly non-additive structure activity effects exist. The QMOD method offers a means to go beyond non-causative correlations in QSAR analysis.

Keywords

QSAR Ligand based modeling Similarity Docking 

References

  1. 1.
    Langham JJ, Cleves AE, Spitzer R, Kirshner D, Jain AN (2009) J Med Chem 52:6107CrossRefGoogle Scholar
  2. 2.
    Alzate-Morales JH, Caballero J, Vergara Jague A, Gonzalez Nilo FD (2009) J Chem Inf Model 49:886CrossRefGoogle Scholar
  3. 3.
    Jain AN, Dietterich TG, Lathrop RH, Chapman D, Critchlow RE, Bauer BE, Webster TA, Lozano-Perez T (1994) J Comput Aided Mol Des 8:635CrossRefGoogle Scholar
  4. 4.
    Jain AN, Koile K, Chapman D (1994) J Med Chem 37:2315CrossRefGoogle Scholar
  5. 5.
    Jain AN, Harris NL, Park JY (1995) J Med Chem 38:1295CrossRefGoogle Scholar
  6. 6.
    Dietterich TG, Lathrop RH, Lozano-Perez T (1997) Artif Intell 89:31CrossRefGoogle Scholar
  7. 7.
    Jain AN (1996) J Comput Aided Mol Des 10:427CrossRefGoogle Scholar
  8. 8.
    Pham TA, Jain AN (2006) J Med Chem 49:5856CrossRefGoogle Scholar
  9. 9.
    Pham TA, Jain AN (2008) J Comput Aided Mol Des 22:269CrossRefGoogle Scholar
  10. 10.
    Johnson SR (2008) J Chem Inf Model 48:25CrossRefGoogle Scholar
  11. 11.
    Cramer RD (2003) J Med Chem 46:374CrossRefGoogle Scholar
  12. 12.
    Cramer RD, Patterson DE, Bunce JD (1988) J Am Chem Soc 110:5959CrossRefGoogle Scholar
  13. 13.
    Cramer RD, Wendt B (2007) J Comput Aided Mol Des 21:23CrossRefGoogle Scholar
  14. 14.
    Klebe G, Abraham U, Mietzner T (1994) J Med Chem 37:4130CrossRefGoogle Scholar
  15. 15.
    Guner O, Clement O, Kurogi Y (2004) Curr Med Chem 11:2991Google Scholar
  16. 16.
    Snyder JP, Rao SN (1989) Chem Design Automation News 4:13Google Scholar
  17. 17.
    Vedani A, Zbinden P, Snyder JP (1993) J Recept Res 13:163Google Scholar
  18. 18.
    Zbinden P, Dobler M, Folkers G, Vedani A (1998) QSAR 17:122Google Scholar
  19. 19.
    Tanrikulu Y, Schneider G (2008) Nat Rev Drug Discov 7:667CrossRefGoogle Scholar
  20. 20.
    Nordvall G, Sundquist S, Johansson G, Glas G, Nilvebrant L, Hacksell U (1996) J Med Chem 39:3269CrossRefGoogle Scholar
  21. 21.
    Johansson G, Sundquist S, Nordvall G, Nilsson BM, Brisander M, Nilvebrant L, Hacksell U (1997) J Med Chem 40:3804CrossRefGoogle Scholar
  22. 22.
    Benson ML, Smith RD, Khazanov NA, Dimcheff B, Beaver J, Dresslar P, Nerothin J, Carlson HA (2008) Nucleic Acids Res 36:D674CrossRefGoogle Scholar
  23. 23.
    Nilvebrant L, Gillberg PG, Sparf B (1997) Pharmacol Toxicol 81:169CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Bioengineering and Therapeutic SciencesHelen Diller Family Comprehensive Cancer Center, University of CaliforniaSan FranciscoUSA

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