Comparative evaluation of 11 scoring functions for molecular docking.
Abstract
Eleven popular scoring functions have been tested on 100 protein-ligand complexes to evaluate their abilities to reproduce experimentally determined structures and binding affinities. They include four scoring functions implemented in the LigFit module in Cerius2 (LigScore, PLP, PMF, and LUDI), four scoring functions implemented in the CScore module in SYBYL (F-Score, G-Score, D-Score, and ChemScore), the scoring function implemented in the AutoDock program, and two stand-alone scoring functions (DrugScore and X-Score). These scoring functions are not tested in the context of a particular docking program. Instead, conformational sampling and scoring are separated into two consecutive steps. First, an exhaustive conformational sampling is performed by using the AutoDock program to generate an ensemble of docked conformations for each ligand molecule. This conformational ensemble is required to cover the entire conformational space as much as possible rather than to focus on a few energy minima. Then, each scoring function is applied to score this conformational ensemble to see if it can identify the experimentally observed conformation from all of the other decoys. Among all of the scoring functions under test, six of them, i.e., PLP, F-Score, LigScore, DrugScore, LUDI, and X-Score, yield success rates higher than the AutoDock scoring function. The success rates of these six scoring functions range from 66% to 76% if using root-mean-square deviation < or =2.0 A as the criterion. Combining any two or three of these six scoring functions into a consensus scoring scheme further improves the success rate to nearly 80% or even higher. However, when applied to reproduce the experimentally determined binding affinities of the 100 protein-ligand complexes, only X-Score, PLP, DrugScore, and G-Score are able to give correlation coefficients over 0.50. All of the 11 scoring functions are further inspected by their abilities to construct a descriptive, funnel-shaped energy surface for protein-ligand complexation. The results indicate that X-Score and DrugScore perform better than the other ones at this aspect.
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Scoring functions are typically divided into three main classes: force field-based, knowledge-based, and empirical.
“Scoring functions are typically divided into three main classes (Wang et al., 2003): force field-based, knowledge-based, and empirical.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Wang et al., 2003
Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
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 · see citing works
A new force field for molecular mechanical simulation of nucleic acids and proteins
Weiner, S. J., Kollman, P. A., Case, D. A., Singh, U. C., Ghio, C., Alagona, G., Profeta, S., & Weiner, P. K. (1984) · Journal of the American Chemical Society · doi:10.1021/ja00315a051 · Cited by 4,641 · see citing works
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 2,836 · see citing works
Eldridge, M., Murray, C. W., Auton, T. R., Paolini, G. V., & Mee, R. P. (1997) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1007996124545 · Cited by 1,692 · see citing works
Wang, R., Lai, L., & Wang, S. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1016357811882 · Cited by 1,185 · see citing works
Self-citationKnowledge-based scoring function to predict protein-ligand interactions
Gohlke, H., Hendlich, M., & Klebe, G. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3371 · Cited by 1,159 · see citing works
B�hm, H.-J. (1994) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00126743 · Cited by 1,013 · see citing works
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 1,008 · see citing works
Bissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740 · see citing works
Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990352k · Cited by 698 · see citing works
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 · see citing works
Detailed Analysis of Scoring Functions for Virtual Screening
Ståhl, M., & Rarey, M. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm0003992 · Cited by 490 · see citing works
Böhm, H. (1998) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1007999920146 · Cited by 431 · see citing works
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 · see citing works
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 312 · see citing works
Self-citationBiosynthetic origin of the carbon skeleton and oxygen atoms of nargenicin A1
Cane, D. E., & Yang, C. C. (1984) · Journal of the American Chemical Society · doi:10.1021/ja00315a052 · Cited by 263 · see citing works
SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex
Wang, R., Liu, L., Lai, L., & Youqi, T. (1998) · Journal of Molecular Modeling · doi:10.1007/s008940050096 · Cited by 249 · see citing works
Self-citationIshchenko, A. V., & Shakhnovich, E. I. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm0105833 · Cited by 157 · see citing works
Protein folding: the free energy surface
Gruebele, M. (2002) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(02)00304-4 · Cited by 124 · see citing works
A knowledge-based scoring function for protein-ligand interactions: Probing the reference state
Muegge, I. (2000) · Perspectives in Drug Discovery and Design · doi:10.1023/a:1008729005958 · Cited by 120 · see citing works
ConsDock: A new program for the consensus analysis of protein–ligand interactions
Paul, N., & Rognan, D. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10119 · Cited by 119 · see citing works
Effect of ligand volume correction on PMF scoring
Muegge, I. (2001) · Journal of Computational Chemistry · doi:10.1002/1096-987x(200103)22:4 · Cited by 111 · see citing works
Terp, G. E., Johansen, B. N., Christensen, I. T., & Jørgensen, F. S. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm001090l · Cited by 97 · see citing works
Parrill, A. L., & Reddy, M. R. (1999) · ACS symposium series · doi:10.1021/bk-1999-0719 · Cited by 89 · see citing works
Gehlhaar, D. K., Bouzida, D., & Rejto, P. A. (1999) · ACS symposium series · doi:10.1021/bk-1999-0719.ch019 · Cited by 81 · see citing works
A knowledge-based scoring function for protein-ligand interactions: Probing the reference state
Muegge, I. (2005) · Kluwer Academic Publishers eBooks · doi:10.1007/0-306-46883-2_7 · Cited by 80 · see citing works
Tertiary and quaternary conformational changes in aspartate transcarbamylase: a normal mode study
Thomas, A. M., Hinsen, K., Field, M. J., & Pérahia, D. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990101)34:1 · Cited by 64 · see citing works
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Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057 · see citing works
Role of computer-aided drug design in modern drug discovery
Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762 · see citing works
Halogen atoms in the modern medicinal chemistry: hints for the drug design
Hernandes, M., Cavalcanti, S. M., Moreira, D. R., de Azevedo Junior, W., & Leite, A. C. (2010) · Current Drug Targets · doi:10.2174/138945010790711996 · Cited by 723 · see citing works
Comparative evaluation of eight docking tools for docking and virtual screening accuracy
Kellenberger, E., Rodrigo, J., Muller, P., & Rognan, D. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20149 · Cited by 577 · see citing works
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 · see citing works
Huang, S.-Y., Grinter, S. Z., & Zou, X. (2010) · Physical Chemistry Chemical Physics · doi:10.1039/c0cp00151a · Cited by 508 · see citing works
Forging the basis for developing protein-ligand interaction scoring functions.
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., & Wang, R. (2017) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00491 · Cited by 493 · see citing works
Self-citationVariability in docking success rates due to dataset preparation.
Corbeil, C. R., Williams, C. I., & Labute, P. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9570-1 · Cited by 469 · see citing works
Binding MOAD (Mother Of All Databases)
Hu, L., Benson, M. L., Smith, R. D., Lerner, M. G., & Carlson, H. A. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20512 · Cited by 328 · see citing works
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331 · see citing works
Nguyen, D. D., Cang, Z., Wu, K., Wang, M., Cao, Y., & Wei, G.-W. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0146-6 · Cited by 155 · see citing works
In silico repurposing of antipsychotic drugs for Alzheimer’s disease
Kumar, S., Chowdhury, S., & Kumar, S. (2017) · BMC Neuroscience · doi:10.1186/s12868-017-0394-8 · Cited by 103 · see citing works
WScore: a flexible and accurate treatment of explicit water molecules in ligand-receptor docking.
Murphy, R. B., Repasky, M. P., Greenwood, J. R., Tubert-Brohman, I., Jerome, S., Annabhimoju, R., Boyles, N. A., Schmitz, C. D., Abel, R., Farid, R., et al. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00131 · Cited by 89 · see citing works
Robust scoring functions for protein-ligand interactions with quantum chemical charge models.
Wang, J.-C., Lin, J.-H., Chen, C.-M., Perryman, A. L., & Olson, A. J. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200220v · Cited by 54 · see citing works
Gao, Y.-D., Hu, Y., Crespo, A., Wang, D., Armacost, K. A., Fells, J. I., Fradera, X., Wang, H., Wang, H., Sherborne, B., et al. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0072-z · Cited by 9 · see citing works
- Authors
- Wang, R., Lu, Y., & Wang, S.
- Affiliations
- University of Michigan (US)
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2003-05-08
- Volume
- 46
- Issue
- 12
- Pages
- 2287-2303
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W1972148009
- Cited by
- 849
- References
- 29
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 20 · 2023: 23 · 2022: 43 · 2021: 21 · 2020: 28 · 2019: 37 · 2018: 43 · 2017: 36 · 2016: 26 · 2015: 41 · 2014: 39 · 2013: 40 · 2012: 36
- FWCI
- 32.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- protein-ligand complex, binding affinity prediction, conformational ensembles, consensus scoring, energy surface, ligand binding, AutoDock, conformational sampling
- MeSH terms
- Algorithms, Chloramphenicol, Diiodothyronines, Ligands, Models, Molecular, Molecular Conformation, Prealbumin, Protein Binding, Proteins, Thermodynamics, Chloramphenicol O-Acetyltransferase, Quantitative Structure-Activity Relationship
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