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Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Open accessAbstract
A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set that includes more diverse targets such as GPCRs and ion channels, totaling 102 proteins with 22886 clustered ligands drawn from ChEMBL, each with 50 property-matched decoys drawn from ZINC. To ensure chemotype diversity, we cluster each target's ligands by their Bemis-Murcko atomic frameworks. We add net charge to the matched physicochemical properties and include only the most dissimilar decoys, by topology, from the ligands. An online automated tool (http://decoys.docking.org) generates these improved matched decoys for user-supplied ligands. We test this data set by docking all 102 targets, using the results to improve the balance between ligand desolvation and electrostatics in DOCK 3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org.
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“The DUD-E dataset is an enhanced version of the original DUD set and has been widely used to train and validate scoring functions (Huang et al., 2006; Mysinger et al., 2012).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Mysinger et al., 2012
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“It is composed of 102 targets with corresponding active, inactive, marginal, and decoy compounds.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Mysinger et al., 2012
In the citing paper
“PDBbind (Liu et al., 2015), DUD-E (Mysinger et al., 2012), and DEKOIS 2.0 (Bauer et al., 2013) are examples of widely used benchmarks for evaluating scoring functions.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Mysinger et al., 2012
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Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593
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
Docking and scoring in virtual screening for drug discovery: methods and applications
Kitchen, D. B., Decornez, H. Y., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,354
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
Self-citationThe Properties of Known Drugs. 1. Molecular Frameworks
Bemis, G. W., & Murcko, M. A. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm9602928 · Cited by 2,501
Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A., & Stahl, M. T. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100031x · Cited by 1,666
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
Self-citationComparison of Shape-Matching and Docking as Virtual Screening Tools
Hawkins, P. C. D., Skillman, A. G., & Nicholls, A. O. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0603365 · Cited by 1,023
The maximal affinity of ligands
Kuntz, I. D., Chen, K., Sharp, K. A., & Kollman, P. A. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.9997 · Cited by 859
Bissantz, 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
Kawahara, T., Konishi, Y., Tada, H., Tohge, N., Nishii, J., & Ito, S. (2002) · Angewandte Chemie International Edition · doi:10.1002/1521-3773(20020802)41:15 · Cited by 513
Detailed Analysis of Scoring Functions for Virtual Screening
Ståhl, M., & Rarey, M. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm0003992 · Cited by 490
Assessing Scoring Functions for Protein−Ligand Interactions
Ferrara, P., Gohlke, H., Price, D. J., Klebe, G., & Brooks, C. L. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030489h · Cited by 488
Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data
Rohrer, S. G., & Baumann, K. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002649 · Cited by 416
Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment
Verdonk, M. L., Berdini, V., Hartshorn, M. J., Mooij, W. T. M., Murray, C. W., Taylor, R. D., & Watson, P. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034289q · Cited by 385
Docking and scoring with ICM: the benchmarking results and strategies for improvement
Neves, M. A. C., Totrov, M. M., & Abagyan, R. A. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9547-0 · Cited by 375
Recommendations for evaluation of computational methods
Jain, A. N., & Nicholls, A. O. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9196-5 · Cited by 350
Rapid Context-Dependent Ligand Desolvation in Molecular Docking
Mysinger, M. M., & Shoichet, B. K. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100214a · Cited by 325
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Carlsson, J., Coleman, R. G., Setola, V., Irwin, J. J., Fan, H., Schlessinger, A., Šali, A., Roth, B. L., & Shoichet, B. K. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.662 · Cited by 309
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Spitzer, R., & Jain, A. N. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9533-y · Cited by 296
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Kolb, P., Rosenbaum, D. M., Irwin, J. J., Fung, J. J., Kobilka, B. K., & Shoichet, B. K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812657106 · Cited by 291
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Irwin, J. J., Shoichet, B. K., Mysinger, M. M., Huang, N., Colizzi, F., Wassam, P., & Cao, Y. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm9006966 · Cited by 287
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Good, A. C., & Oprea, T. I. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9167-2 · Cited by 186
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Cereto‐Massagué, A., Guasch, L., Valls, C., Mulero, M., Pujadas, G., & Garcı́a-Vallvé, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts249 · Cited by 186
Babaoglu, K., Simeonov, A. M., Irwin, J. J., Nelson, M. E., Feng, B. Y., Thomas, C. J., Cancian, L., Costi, M. P., Maltby, D. A., Jadhav, A., et al. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm701500e · Cited by 180
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Brozell, S. R., Mukherjee, S., Balius, T. E., Roe, D. R., Case, D. A., & Rizzo, R. C. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9565-y · Cited by 163
Repasky, M. P., Murphy, R. B., Banks, J. L., Greenwood, J. R., Tubert‐Brohman, I., Bhat, S., & Friesner, R. A. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9575-9 · Cited by 159
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Golovin, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh078 · Cited by 159
Schneider, N., Hindle, S. A., Lange, G., Klein, R., Albrecht, J., Briem, H., Beyer, K., Claußen, H., Gastreich, M., Lemmen, C., et al. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9531-0 · Cited by 154
Parameter Estimation for Scoring Protein−Ligand Interactions Using Negative Training Data
Pham, T. A., & Jain, A. N. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050040j · Cited by 147
Molecular Docking Screens Using Comparative Models of Proteins
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Metadata
- Authors
- Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Irwin, John. J.
- Shoichet, Brian K.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-07-05
- Volume
- 55
- Issue
- 14
- Pages
- 6582-6594
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- PMC
- PMC3405771
- OpenAlex
- W1968319881
- Semantic Scholar
- 3b2ffd1b7ce968924e9046b0a0ff3a1f2b033de9
- Cited by
- 2,412
- References
- 54
- Citations by year
- 2026: 231 · 2025: 285 · 2024: 283 · 2023: 243 · 2022: 279 · 2021: 247 · 2020: 207 · 2019: 162 · 2018: 123 · 2017: 96 · 2016: 101 · 2015: 77 · 2014: 44 · 2013: 28 · 2012: 3
- FWCI
- 52.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Medicinal Chemistry (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- DUD-E, decoy generation, G protein-coupled receptors, ion channels, ligand clustering, net charge, electrostatics, ChEMBL, zinc, decoy database
- MeSH terms
- Humans, Ligands, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Reference Standards, Cluster Analysis, Benchmarking, Drug Discovery
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