The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Improved protein–ligand docking using GOLD
Abstract
The Chemscore function was implemented as a scoring function for the protein–ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, “Goldscore‐CS” and “Chemscore‐GS,” in terms of docking accuracy, prediction of binding affinities, and speed. In the “Goldscore‐CS” protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the “Chemscore‐GS” protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a “clean” set of 224 protein–ligand complexes, and for two subsets of this set, one for which the ligands are “drug‐like,” the other for which they are “fragment‐like.” For “drug‐like” and “fragment‐like” ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. “Goldscore‐CS” gives success rates of up to 81% (top‐ranked GOLD solution within 2.0 Å of the experimental binding mode) for the “clean list,” but at the cost of long search times. For most virtual screening applications, “Chemscore‐GS” seems optimal; search settings that give docking speeds of around 0.25–1.3 min/compound have success rates of about 78% for “drug‐like” compounds and 85% for “fragment‐like” compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1–2 min/compound, the Goldscore function predicts binding energies with a standard deviation of ∼10.5 kJ/mol. Proteins 2003;52:609–623. © 2003 Wiley‐Liss, Inc.
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Molecular properties that influence the oral bioavailability of drug candidates
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Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
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Cited by
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
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Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery
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Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva-Jr, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 641
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Metadata
- Authors
- Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W., & Taylor, R. D.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2003-08
- Volume
- 52
- Issue
- 4
- Pages
- 609-623
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2052030759
- Cited by
- 3,067
- References
- 4
- FWCI
- 44.80
- Type
- journal article
- Language
- en
- Open access
- closed
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