The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results
Abstract
Our comparative assessment of scoring functions (CASF) benchmark is created to provide an objective evaluation of current scoring functions. The key idea of CASF is to compare the general performance of scoring functions on a diverse set of protein-ligand complexes. In order to avoid testing scoring functions in the context of molecular docking, the scoring process is separated from the docking (or sampling) process by using ensembles of ligand binding poses that are generated in prior. Here, we describe the technical methods and evaluation results of the latest CASF-2013 study. The PDBbind core set (version 2013) was employed as the primary test set in this study, which consists of 195 protein-ligand complexes with high-quality three-dimensional structures and reliable binding constants. A panel of 20 scoring functions, most of which are implemented in main-stream commercial software, were evaluated in terms of "scoring power" (binding affinity prediction), "ranking power" (relative ranking prediction), "docking power" (binding pose prediction), and "screening power" (discrimination of true binders from random molecules). Our results reveal that the performance of these scoring functions is generally more promising in the docking/screening power tests than in the scoring/ranking power tests. Top-ranked scoring functions in the scoring power test, such as X-Score(HM), ChemScore@SYBYL, ChemPLP@GOLD, and PLP@DS, are also top-ranked in the ranking power test. Top-ranked scoring functions in the docking power test, such as ChemPLP@GOLD, Chemscore@GOLD, GlidScore-SP, LigScore@DS, and PLP@DS, are also top-ranked in the screening power test. Our results obtained on the entire test set and its subsets suggest that the real challenge in protein-ligand binding affinity prediction lies in polar interactions and associated desolvation effect. Nonadditive features observed among high-affinity protein-ligand complexes also need attention.
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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
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
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058
Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412
Benchmarking sets for molecular docking.
Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318
The PDBbind database: methodologies and updates
Wang, R., Fang, X., Lu, Y., Yang, C.-Y., & Wang, S. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm048957q · Cited by 933
Self-citationComparative evaluation of 11 scoring functions for molecular docking.
Wang, R., Lu, Y., & Wang, S. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0203783 · Cited by 849
Self-citationBissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740
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
Damm-Ganamet, K. L., Smith, R. D., Dunbar, J. B., Stuckey, J. A., & Carlson, H. A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400025f · Cited by 133
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
Cited by
Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp01555g · Cited by 946
Self-citationRegulation of protein-ligand binding affinity by hydrogen bond pairing
Chen, D., Oezguen, N., Urvil, P., Ferguson, C., Dann, S. M., & Savidge, T. C. (2016) · Science Advances · doi:10.1126/sciadv.1501240 · Cited by 931
Computational methods in drug discovery
Leelananda, S. P., & Lindert, S. (2016) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.12.267 · Cited by 639
Use of molecular docking computational tools in drug discovery
(2021) · Progress in Medicinal Chemistry · doi:10.1016/bs.pmch.2021.01.004 · Cited by 621
Sun, H., Li, Y., Shen, M., Tian, S., Xu, L., Pan, P., Guan, Y., & Hou, T. (2014) · Phys. Chem. Chem. Phys. · doi:10.1039/c4cp03179b · Cited by 526
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Metadata
- Authors
- Li, Y., Han, L., Liu, Z., & Wang, R.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-02
- Volume
- 54
- Issue
- 6
- Pages
- 1717-1736
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2013085020
- Cited by
- 382
- References
- 12
- FWCI
- 27.80
- Type
- journal article
- Language
- en
- Open access
- closed
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