Exploiting structure-activity relationships in docking

Journal of Chemical Information and Modeling

Volumn 48, Issue 4, 2008, Pages 817-830

  Sullivan, David C ^a   Martin, Eric J ^a  

^a NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CRYSTAL STRUCTURE; ENZYME ACTIVITY; ERROR ANALYSIS; MATHEMATICAL MODELS;

CHEMICAL DESCRIPTORS; MULTIPLE SCORING FUNCTIONS;

CHEMICAL ACTIVATION;

PHOSPHOTRANSFERASE; PROTEINASE;

ARTICLE; CHEMICAL STRUCTURE; DRUG DESIGN; METABOLISM; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

DRUG DESIGN; HIV PROTEASE; MOLECULAR STRUCTURE; PHOSPHOTRANSFERASES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 44449170479     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci700439z     Document Type: Article

References (41)

1. Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin, T. E. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 1982, 161, 269-288.
2. Warren, G. L.; Andrews, C. W.; Capelli, A. M.; Clarke, B.; Lalonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. A Critical Assessment of Docking Programs and Scoring Functions. J. Med. Chem. 2006, 49, 5912-5931.
3. Chen, H.; Lyne, P. D.; Giordanetto, F.; Lovell, T.; Li, J. On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors. J. Chem. Inf. Model. 2006, 46, 401-415.
4. Perola, E.; Walters, W. P.; Charifson, P. S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins: Struct, Funct., Bioinf 2004, 56, 235-249.
5. Ha, S.; Andreani, R.; Robbins, A.; Muegge, I. Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors. J. Comput.-Aided Mol Des. 2000, 14, 435-448.
6. Bissantz, C.; Folkers, G.; Rognan, D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J. Med. Chem. 2000, 43, 4759-4767.
7. Good, A. C.; Cheney, D. L.; Sifkoff, D. F.; Tokarski, J. S.; Stouch, T. R.; Bassolino, D. A.; Krystek, S. R.; Li, Y.; Mason, J. S.; Perkins, T. D. Analysis and optimization of structure-based virtual screening protocols. 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success. J. Mol. Graphics Modell. 2003, 22, 31-40.
8. Ewing, T. J. A.; Kuntz, I. D. Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comput. Chem. 1997, 18, 1175-1189.
9. Ajay; Murcko, M. A. Computational methods to predict binding free energy in ligand-receptor complexes. J. Med. Chem. 1995, 38, 4953-4967.
10. Patterson, D. E.; Cramer, R. D.; Ferguson, A. M.; Clark, R. D.; Weinberger, L. E. Neighborhood behavior. A useful concept for validation of "molecular diversity" descriptors. J. Med. Chem. 1996, 39, 3049-3059.
11. Martin, Y. C.; Kofron, J. L.; Traphagen, L. M. Do structurally similar molecules have similar biological activity. J. Med. Chem. 2002, 45, 4350-4358.
12. Kubinyi, H. Similarity and dissimilarity: A medicinal chemist's view. Perspect Drug Discovery 1998, 9-11, 225-252.
13. Walters, W. P.; Goldman, B. B. Feature selection in quantitative structure-activity relationships. Curr. Opin. Drug Discovery Dev. 2005, 8, 329-333.
14. Hansen, C.; Fujita, T. Rho-Sigma-Pi-Anatysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 1964, 86, 1616-1626.
15. Topliss, J. G.; Costello, R. J. Chance correlations in structure-activity studies using multiple regression analysis. J. Med. Chem. 1972, 15, 1066-1068.
16. Bohacek, R. S.; McMartin, C.; Guida, W. C. The art and practice of structure-based drug design: A molecular modeling perspective. Med. Res. Rev. 1996, 16, 3-50.
17. Stone, M. Cross-Validatory Choice and Assessment of Statistical Predictions. J. Royal Stat. Soc. Ser. B 1974, 36, 111-147.
18. Geisser, S.; Eddy, W. F. A Predictive Approach to Model Selection. J. Am. Stat Assoc. 1979, 74, 153-160.
19. Glick, M.; Jenkins, J. L.; Nettles, J. H.; Hitchings, H.; Davies, J. W. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and Laplacian-modified naive Bayesian classifiers. J. Chem. Inf. Model. 2006, 46, 193-200.
20. Klon, A. E.; Glick, M.; Thoma, M.; Acklin, P.; Davies, J. W. Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results. J. Med. Chem. 2004, 47, 2743-2749.
21. Krumrine, J. R.; Maynard, A. T.; Lerman, C. L. Statistical tools for virtual screening. J. Med. Chem. 2005, 48, 7477-7481.
22. Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks. J. Med. Chem. 1996, 39, 2887-2893.
23. Chen, X.; Lin, Y.; Liu, M.; Gilson, M. K. The Binding Database: data management and interface design. Bioinformatics 2002, 18, 130-139.
24. Wold, H. Estimation of principal components and related models by iterative least squares. In Multivariate Analysis; Krishnaiah, P. R., Ed.; Academic Press: New York, U.S.A., 1966; pp 391-420.
25. Pipeline Pilot, version 6.0; Scitegic: San Diego, CA, 2006.
26. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235-242.
27. Docklt, version 1.5; Metaphorics LLC: Santa Fe, NM, 2001.
28. Magnet, version 1; Metaphorics LLC: Santa Fe, NM, 2001.
29. Vieth, M.; Higgs, R. E.; Robertson, D. H.; Shapiro, M.; Gragg, E. A.; Hemmerle, H. Kinomics-structural biology and chemogenomics of kinase inhibitors and targets. Biochim. Biophys. Acta, Proteins Proteomics 2004, 7697, 243-257.
30. ter Haar, E.; Walters, W. P.; Pazhanisamy, S.; Taslimi, P.; Pierce, A. C.; Bemis, G. W.; Salituro, F. G.; Harbeson, S. L. Kinase chemogenomics: Targeting the human kinome for target validation and drug discovery. Mini-Rev. Med. Chem. 2004, 4, 235-253.
31. McMartin, C.; Bohacek, R. S. QXP: Powerful, rapid computer algorithms for structure-based drug design. J. Comput-Aided Mol. Des. 1997, 11, 333-344.
32. Martin, E. J.; Sullivan, D. C. Pre-docking into a Universal Ensemble Kinase Receptor for 3D activity prediction, very quickly, without a crystal structure J. Chem. Inf. Model. 2008, accepted for publication.
33. Gehlhaar, D. K.; Verkhivker, G. M.; Rejto, P. A.; Sherman, C. J.; Fogel, D. B.; Fogel, L. J.; Freer, S. T. Molecular recognition of the inhibitor AG-1343 by HTV-1 protease: conformationally flexible docking by evolutionary programrning. Chem. Biol. 1995, 2, 317-324.
34. Muegge, I.; Martin, Y. C. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J. Med. Chem. 1999, 42, 791-804.
35. Yoon, S.; Smellie, A.; Hartsough, D.; Filikov, A. Surrogate docking: structure-based virtual screening at high throughput speed. J. Comput-Aided Mol. Des. 2005, 19, 483-497.
36. Taylor, R. Simulation Analysis of Experimental Design Strategies for Screening Random Compounds as Potential New Drugs and Agrochemicals. J. Chem. Inf. Comput. Sci. 1995, 35, 59-67.
37. Schneider, G.; Schneider, P.; Renner, S. Scaffold-Hopping: How Far Can You Jump. QSAR Comb. Sci. 2006, 25, 1162-1171.
38. Teig, S. L. Point - Informative libraries are more useful than diverse ones. J. Biomol. Screening 1998, 3, 85-88.
39. Martin, E. J.; Critchlow, R. E. Beyond mere diversity: Tailoring combinatorial libraries for drug discovery. J. Comb. Chem. 1999, 1, 32-45.
40. Miller, J. L. Recent developments in focused library design: Targeting gene-families. Curr. Top. Med. Chem. 2006, 6, 19-29.
41. Malinowski, E. R. Theory of error in factor analysis. Anal. Chem. 1977, 49, 606-612.