The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Evaluation of Docking Performance: Comparative Data on Docking Algorithms
Abstract
Docking molecules into their respective 3D macromolecular targets is a widely used method for lead optimization. However, the best known docking algorithms often fail to position the ligand in an orientation close to the experimental binding mode. It was reported recently that consensus scoring enhances the hit rates in a virtual screening experiment. This methodology focused on the top-ranked pose, with the underlying assumption that the orientation/conformation of the docked compound is the most accurate. In an effort to eliminate the scoring function bias, and assess the ability of the docking algorithms to provide solutions similar to the crystallographic modes, we investigated the most known docking programs and evaluated all of the resultant poses. We present the results of an extensive computational study in which five docking programs (FlexX, DOCK, GOLD, LigandFit, Glide) were investigated against 14 protein families (69 targets). Our findings show that some algorithms perform consistently better than others, and a correspondence between the nature of the active site and the best docking algorithm can be found.
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Cites
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
Charge equilibration for molecular dynamics simulations
Rappe, A. K., & Goddard, W. A. (1991) · The Journal of Physical Chemistry · doi:10.1021/j100161a070 · Cited by 3,465
A Fast Flexible Docking Method using an Incremental Construction Algorithm
Rarey, M., Kramer, B., Lengauer, T., & Klebe, G. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0477 · Cited by 2,827
Analytical molecular surface calculation
Connolly, M. L. (1983) · Journal of Applied Crystallography · doi:10.1107/s0021889883010985 · Cited by 2,508
A geometric approach to macromolecule-ligand interactions
Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982) · Journal of Molecular Biology · doi:10.1016/0022-2836(82)90153-x · Cited by 2,278
The statistical-thermodynamic basis for computation of binding affinities: a critical review
Gilson, M. K., Given, J. A., Bush, B. L., & McCammon, J. A. (1997) · Biophysical Journal · doi:10.1016/s0006-3495(97)78756-3 · Cited by 1,220
B�hm, H.-J. (1994) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00126743 · Cited by 1,011
A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
Muegge, I., & Martin, Y. C. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm980536j · Cited by 1,007
LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites
Venkatachalam, C. M., Jiang, X., Oldfield, T., & Waldman, M. (2003) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(02)00164-x · Cited by 1,002
Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking
Kramer, B., Rarey, M., & Lengauer, T. (1999) · Proteins: Structure, Function, and Genetics · doi:10.1002/(sici)1097-0134(19991101)37:2<228::aid-prot8>3.0.co;2-8 · Cited by 906
Bissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740
Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990352k · Cited by 698
Gehlhaar, D. K., Verkhivker, G. M., Rejto, P. A., Sherman, C. J., Fogel, D. R., Fogel, L. J., & Freer, S. T. (1995) · Chemistry & Biology · doi:10.1016/1074-5521(95)90050-0 · Cited by 684
Detailed Analysis of Scoring Functions for Virtual Screening
Stahl, M., & Rarey, M. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm0003992 · Cited by 489
A new test set for validating predictions of protein–ligand interaction
Nissink, J. W. M., Murray, C., Hartshorn, M., Verdonk, M. L., Cole, J. C., & Taylor, R. (2002) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10232 · Cited by 425
Consensus scoring for ligand/protein interactions
Clark, R. D., Strizhev, A., Leonard, J. M., Blake, J. F., & Matthew, J. B. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00125-5 · Cited by 401
Combined Quantum Mechanical/Molecular Mechanical Methodologies Applied to Biomolecular Systems
Monard, G., & Merz, K. M. (1999) · Accounts of Chemical Research · doi:10.1021/ar970218z · Cited by 336
Structure-Based Discovery of Inhibitors of Thymidylate Synthase
Shoichet, B. K., Stroud, R. M., Santi, D. V., Kuntz, I. D., & Perry, K. M. (1993) · Science · doi:10.1126/science.8451640 · Cited by 322
How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment
Wang, R., & Wang, S. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010025x · Cited by 311
Bodian, D. L., Yamasaki, R. B., Buswell, R. L., Stearns, J. F., White, J. M., & Kuntz, I. D. (1993) · Biochemistry · doi:10.1021/bi00063a007 · Cited by 184
Debnath, A. K., Radigan, L., & Jiang, S. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990154t · Cited by 163
De Novo Design of Enzyme Inhibitors by Monte Carlo Ligand Generation
Gehlhaar, D. K., Moerder, K. E., Zichi, D. A., Sherman, C. J., Ogden, R. C., & Freer, S. T. (1995) · Journal of Medicinal Chemistry · doi:10.1021/jm00003a010 · Cited by 122
Assessing search strategies for flexible docking
Vieth, M., Hirst, J. D., Dominy, B. N., Daigler, H., & Brooks, C. L. (1998) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(19981115)19:14<1623::aid-jcc8>3.0.co;2-l · Cited by 113
Evaluation of the CASP2 docking section
Dixon, J. S. (1997) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(1997)1+<198::aid-prot26>3.0.co;2-i · Cited by 103
Parrill, A. L., & Reddy, M. R. (1999) · ACS symposium series · doi:10.1021/bk-1999-0719 · Cited by 90
On the use of quantum energy surfaces in the derivation of molecular force fields
Hagler, A. T., & Ewig, C. S. (1994) · Computer Physics Communications · doi:10.1016/0010-4655(94)90208-9 · Cited by 84
Gehlhaar, D. K., Bouzida, D., & Rejto, P. A. (1999) · ACS symposium series · doi:10.1021/bk-1999-0719.ch019 · Cited by 82
Inhibitors of Kinesin Activity from Structure-Based Computer Screening
Hopkins, S. C., Vale, R. D., & Kuntz, I. D. (2000) · Biochemistry · doi:10.1021/bi992474k · Cited by 81
Structure-Based Discovery of Ligands Targeted to the RNA Double Helix
Chen, Q., Shafer, R. H., & Kuntz, I. D. (1997) · Biochemistry · doi:10.1021/bi970756j · Cited by 72
Cited by
Software for molecular docking: a review
Pagadala, N. S., Syed, K., & Tuszynski, J. (2017) · Biophysical Reviews · doi:10.1007/s12551-016-0247-1 · Cited by 1,522
Validation of Molecular Docking Programs for Virtual Screening against Dihydropteroate Synthase
Hevener, K. E., Zhao, W., Ball, D. M., Babaoglu, K., Qi, J., White, S. W., & Lee, R. E. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800293n · Cited by 668
Molecular dynamics simulations and novel drug discovery
Liu, X., Shi, D., Zhou, S., Liu, H., Liu, H., & Yao, X. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1403419 · Cited by 518
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707515 · Cited by 514
Validation Studies of the Site-Directed Docking Program LibDock
Rao, S. N., Head, M. S., Kulkarni, A., & LaLonde, J. M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6004299 · Cited by 408
Metadata
- Authors
- Kontoyianni, M., McClellan, L. M., & Sokol, G. S.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2003-12-31
- Volume
- 47
- Issue
- 3
- Pages
- 558-565
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2068017935
- Cited by
- 600
- References
- 29
- FWCI
- 23.31
- Type
- journal article
- Language
- en
- Open access
- closed
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