Combination of a naive bayes classifier with consensus scoring improves enrichment of high-throughput docking results

Journal of Medicinal Chemistry

Volumn 47, Issue 18, 2004, Pages 4356-4359

  Klon, Anthony E ^a   Glick, Meir ^a   Davies, John W ^a  

^a NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLINICAL PROTOCOL; COMBINATORIAL CHEMISTRY; DRUG CLASSIFICATION; LEARNING; MACHINE; MOLECULAR MECHANICS; NAIVE BAYES CLASSIFIER; PROTEIN BINDING; SCORING SYSTEM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATABASES, PROTEIN; DRUG DESIGN; MODELS, STATISTICAL; PROTEIN BINDING; PROTEINS;

EID: 4143132047     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm049970d     Document Type: Article

References (14)

1. Klon, A. E.; Glick, M.; Thoma, M.; Acklin, P.; Davies, J. W. Finding more needles in the haystack: a simple and efficient method of improving high-throughput docking results. J. Med. Chem. 2004, 47, 2743-2749.
2. Pipeline Pilot 3.0; Scitegic, Inc. (9665 Chesapeake Drive, Suite 401, San Diego, CA 92123), 2003.
3. Charifson, P. S.; Corkery, J. J.; Murcko, M. A.; Walters, W. P. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem. 1999, 42, 5100-5109.
4. Tripos, Inc. (1699 South Hanley Road, St. Louis, MO 63144), 2003.
5. Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 1997, 267, 727-748.
6. 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.
7. 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.
8. Eldridge, M. D.; Murray, C. W.; Auton, T. R.; Paolini, G. V.; Mee, R. P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput.-Aided Mol. Des 1997, 11, 425-445.
9. Clark, R. D.; Strizhev, A.; Leonard, J. M.; Blake, J. F.; Matthew, J. B. Consensus scoring for ligand/protein interactions. J. Mol. Graphics Modell. 2002, 20, 281-295.
10. Wang, R.; Wang, S. How does consensus scoring work for virtual library screening? An idealized computer experiment. J. Chem. Inf. Comput. Sci. 2001, 41, 1422-1426.
11. Bohm, H. J.; Stahl, M. The use of scoring functions in drug discovery applications. In Reviews in Computational Chemistry; Lipkowitz, K. B., Boyd, D. B., Eds.; John Wiley & Sons: Hoboken, NJ, 2002; pp 41-87.
12. Witten, I. H.; Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations; Morgan Kaufmann Publishers: New York, 1999.
13. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 1996, 261, 470-489.
14. Jacobsson, M.; Liden, P.; Stjernschantz, E.; Bostrom, H.; Norinder, U. Improving structure-based virtual screening by multivariate analysis of scoring data. J. Med. Chem. 2003, 46, 5781-5789.