The effect of structural redundancy in validation sets on virtual screening performance

Journal of Chemometrics

Volumn 23, Issue 9, 2009, Pages 471-478

  Clark, Robert D ^a,b   Shepphird, Jennifer K ^c   Holliday, John ^d  

^a Biochemical Infometrics   (United States)

^b INDIANA UNIVERSITY   (United States)

^c Tripos International   (United States)

^d UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Circular fingerprints; Clustering; Docking; ROC; Validation

Indexed keywords

CIRCULAR FINGERPRINT; CLUSTERINGS; DOCKING; RECEIVER OPERATING CHARACTERISTICS; SCREENING PERFORMANCE; STRUCTURAL REDUNDANCY; VALIDATION; VALIDATION SETS; VIRTUAL SCREENING;

REDUNDANCY;

EID: 70349601754     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1240     Document Type: Article

References (42)

1. Kellenberger E, Rodrigo J, Muller P, Rognan D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins: Struct. Funct. Bioinform. 2004; 57: 225-242.
2. Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CL III. Assessing scoring functions for protein-ligand interactions. J. Med. Chem. 2004; 47: 3032-3047.
3. Warren GL, Andrews CW, Capeli A-M, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS. A critical assessment of docking programs and scoring functions. J. Med. Chem. 2006; 49: 5912-5931.
4. Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J. Med. Chem. 2006; 49: 6789-6801.
5. Jain AN. Morphological similarity: a 3D molecular similarity method correlated with protein-ligand recognition. J. Comput. Aided Mol. Des. 2000; 14: 199-213.
6. Cuissart B, Touffet F, Cremilleux B, Bureau R, Raul S. The maximum common substructure as a molecular depiction in a supervised classification context: experiments in quantitative structure/biode-gradability relationships. J. Chem. Info. Comput. Sci. 2002; 42: 1043-1052.
7. Jain AN. Ligand-based structural hypotheses for virtual screening. J. Med. Chem. 2004; 47: 947-961.
8. Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O. Virtual screening workflow development guided by the ''receiver operating character-istic'' curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J. Med. Chem. 2005; 48: 2534-2547.
9. Nicholls A. What do we know and when do we know it? J. Comput. Aided Mol. Des. 2008; 22: 239-255.
10. Clark RD. Getting past diversity in assessing virtual library designs. J. Brazil Chem. Soc. 2002; 13: 788-794.
11. Clark RD, Fox PC. Statistical variation in progressive scrambling. J. Comput. Aided Mol. Des. 2004; 18: 563-576.
12. Rohrer SG, Baumann K. Impact of benchmark data set topology on the validation of virtual screening methods: exploration and quantification by spatial statistics. J. Chem. Inf. Model. 2008; 48: 704-718.
13. Egan JP. Signal Detection Theory and ROC Analysis;. Academic Press: New York, NY, 1975.
14. Good AC, Hermsmeier MA, Hindle SA. Measuring CAMD technique performance: a virtual screening case study in the design of validation experiments. J. Comput. Aided Mol. Des. 2004; 18: 529-536.
15. Good AC, Oprea TI. Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J. Comput. Aided Mol. Des. 2008; 22: 169-178.
16. Clark RD, Webster-Clark DJ. Managing bias in ROC curves. J. Comput. Aided Mol. Des. 2008; 22: 141-146.
17. Irwin JJ. DUD: a directory of useful decoys. http://dud.docking.org (accessed 15 July 2007).
18. Jain AN. Surflex-dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput. Aided Mol. Des. 2007; 21: 281-306.
19. Surflex-Dock, version 2.3; Biopharmics LLC: San Francisco, CA, 2008.
20. Verdonk ML, Berdini V, Hartshorn MJ, Mooij WT, Murray CW, Taylor RD, Watson P. Virtual screening using protein-ligand docking: avoiding artificial enrichment. J. Chem. Inf. Comput. Sci. 2004; 44: 793-806.
21. Bender A, Glen RC. A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication. J. Chem. Inf. Model. 2005; 45: 1369-1375.
22. MDL Drug Data Report. http://www.mdli.com/products/pdfs/mddr_ds.pdf [25 March 2008].
23. Schuffenhauer A, Zimmermann J, Stoop R, van der Vyver J-J, Lecchini S, Jacoby E. An ontology for pharmaceutical ligands and its appli-cation for in silico screening and library design. J. Chem. Inf. Comput. Sci. 2002; 42: 947-955.
24. Hert J, Willett P, Wilton DJ. New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J. Chem. Inf. Model. 2006; 46: 462-470.
25. Pearlman RS, Rusinko A, Skell JM, Balducci R. Concord; Tripos International: St. Louis, MO, 2008.
26. SciTegic Pipeline Pilot, version 6.1; Accelrys: San Diego, CA, 2008.
27. Hert J, Willett P, Wilton DJ, 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. 2004; 2: 3256-3266.
28. Ash S, Cline MA, Homer RW, Hurst T, Smith GB. SYBYL line notation (SLN): a versatile language for chemical structure representation. J. Chem Inf. Comput. Sci. 1997; 37: 71-79.
29. SYBYL, version 7.8; Tripos International: St. Louis, MO, 2008.
30. UNITY, version 7.8; Tripos International: St. Louis, MO, 2008.
31. Kaufman L, Rousseeuw PJ. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience: New York, NY, 1990.
32. 2 test. J. Royal. Stat. Soc. 1934; 1: 217-235.
33. Snedecor GW, Cochran WG. Statistical Methods, 8th edn, Iowa State Press: Ames IA, 1989; 119-120.
34. Truchon J-F, Bayly CI. Evaluating virtual screening methods: good and bad metrics for the ''early recognition'' problem. J. Chem. Inf. Model. 2007; 47: 488-508.
35. Sheridan RP, Singh SB, Fluder EM, Kearsley SK. Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. J. Chem. Inf. Comput. Sci. 2001; 41: 1395-1406.
36. Liebeschuetz JW. Evaluating docking programs: keeping the playing field level. J. Comput. Aided Mol. Des. 2008; 22: 229-238.
37. Jain AN. Surflex-dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput. Aided Mol. Des. 2007; 21: 281-306.
38. Cannon EO, Mitchell JBO. Classifying the world anti-doping agency's 2005 prohibited list using the chemistry development kit fingerprint. Lecture Notes Comput. Sci. 2006; 4216: 173-182.
39. Haranczyk M, Holliday J. Comparison of similarity coefficients for clustering and compound selection. J. Chem. Inf. Model. 2008; 48: 498-509.
40. Sheridan RP, Miller MD, Underwood DJ, Simon K, Kearsley SK. Chemical similarity using geometric atom pair descriptors. J. Chem. Inf. Comput. Sci. 1996; 36: 128-136.
41. Bemis GW, Murcko MA. The properties of known drugs 1. molecular frameworks. J. Med. Chem. 2008; 48: 704-718.
42. Ghose AK, Herbertz T, Pippin DA, Salvino JM, Mallamo JP. Knowledge based prediction of ligand binding modes and rational inhibitor design for kinase drug discovery. J. Med. Chem. 2008; 48: 5149-5171.