Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results

  • Robert P. Sheridan
  • Georgia B. McGaughey
  • Wendy D. Cornell


As an extension to a previous published study (McGaughey et al., J Chem Inf Model 47:1504–1519, 2007) comparing 2D and 3D similarity methods to docking, we apply a subset of those virtual screening methods (TOPOSIM, SQW, ROCS-color, and Glide) to a set of protein/ligand pairs where the protein is the target for docking and the cocrystallized ligand is the target for the similarity methods. Each protein is represented by a maximum of five crystal structures. We search a diverse subset of the MDDR as well as a diverse small subset of the MCIDB, Merck’s proprietary database. It is seen that the relative effectiveness of virtual screening methods, as measured by the enrichment factor, is highly dependent on the particular crystal structure or ligand, and on the database being searched. 2D similarity methods appear very good for the MDDR, but poor for the MCIDB. However, ROCS-color (a 3D similarity method) does well for both databases.


2D similarity 3D similarity Docking BEDROC ROC Glide ROCS SQ SQW TOPOSIM 



The authors thank Christopher Bayly for useful discussions.

Supplementary material

10822_2008_9168_MOESM2_ESM.txt (347 kb)
(TXT 348 kb)


  1. 1.
    McGaughey GB, Sheridan RP, Bayly CI, Culberson JC, Kreatsoulas CK, Lindsley S, Maiorov V, Truchon J-F, Cornell WD (2007) Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47:1504–1519CrossRefGoogle Scholar
  2. 2.
    Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 25:64–73Google Scholar
  3. 3.
    MDL Drug Data Report licensed by Molecular Design Ltd., San Leandro, CA.
  4. 4.
    McGann MR, Almond HR, Nicholls A, Grant JA, Brown FK (2003) Gaussian docking functions. Biopolymers 68:76–90CrossRefGoogle Scholar
  5. 5.
    Hawkins PCD (2006) A comparison of structure-based and shape-based tools for virtual screening. Abstracts of Papers, 231st ACS National Meeting, Atlanta, GA, United States, March 26–30, 2006Google Scholar
  6. 6.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759CrossRefGoogle Scholar
  7. 7.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749CrossRefGoogle Scholar
  8. 8.
    Nilakantan R, Bauman N, Dixon JS, Venkataraghavan R (1987) Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors. J Chem Inf Comput Sci 27:82–85CrossRefGoogle Scholar
  9. 9.
    Kearsley SK, Sallamack S, Fluder EM, Andose JD, Mosley RT, Sheridan RP (1996) Chemical similarity using physiochemical property descriptors. J Chem Inf Comput Sci 36:118–127CrossRefGoogle Scholar
  10. 10.
    Miller MD, Sheridan RP, Kearsley SK (1999) SQ: a program for rapidly producing pharmacophorically relevant molecular superpositions. J Med Chem 42:1505–1514CrossRefGoogle Scholar
  11. 11.
    Edgar SJ, Holliday JD, Willett P (2000) Effectiveness of retrieval in similarity searches of chemical databases: a review of performance measures. J Mol Graph Model 18:343–357CrossRefGoogle Scholar
  12. 12.
    Sheridan RP, Singh SB, Fluder EM, Kearsley SK (2001) Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. J Chem Inf Comput Sci 41:1395–1406CrossRefGoogle Scholar
  13. 13.
    Triballeau N, Archer F, Brabet I, Pin J-P, Bertrand H-O (2005) Virtual screening workflow development guided by the ‘receiver operating characteristic’ curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547CrossRefGoogle Scholar
  14. 14.
    Seifert MHJ (2006) Assessing the discriminatory power of scoring functions for virtual screening. J Chem Inf Model 46:1456–1465CrossRefGoogle Scholar
  15. 15.
    Truchon J-F, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the early recognition problem. J Chem Inf Model 47:488–508CrossRefGoogle Scholar
  16. 16.
    Sheridan RP Alternative global goodness metrics and sensitivity analysis: heuristics to check the robustness of conclusions from studies comparing virtual screening methods. J Chem Inf Model (in press)Google Scholar
  17. 17.
    Kairys V, Fernandes MX, Gilson MK (2006) Screening drug-like compounds by docking to homology models: a systematic study. J Chem Inf Model 46:365–379CrossRefGoogle Scholar
  18. 18.
    Erickson JA, Jalaie M, Robertson DH, Lewis RA, Vieth M (2004) Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J Med Chem 47:45–55CrossRefGoogle Scholar
  19. 19.
    Andersson CD, Thysell E, Lindstrom A, Bylesjo M, Raubacher F, Linusson A (2007) A multivariate approach to investigate docking parameters’ effects on docking performance. J Chem Inf Model 47:1673–1687CrossRefGoogle Scholar
  20. 20.
    McGovern SL, Shoichet BK (2003) Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J Med Chem 46:2895–2907CrossRefGoogle Scholar
  21. 21.
    Muegge I, Enyedy IJ (2004) Virtual screening for kinase targets. Curr Med Chem 11:693–707CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Robert P. Sheridan
    • 1
  • Georgia B. McGaughey
    • 2
  • Wendy D. Cornell
    • 1
  1. 1.Molecular Systems DepartmentMerck Research LaboratoriesRahwayUSA
  2. 2.Molecular Systems DepartmentMerck Research LaboratoriesWest PointUSA

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