The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm
Abstract
The influence of various factors on the accuracy of protein‐ligand docking is examined. The factors investigated include the role of a grid representation of protein‐ligand interactions, the initial ligand conformation and orientation, the sampling rate of the energy hyper‐surface, and the final minimization. A representative docking method is used to study these factors, namely, CDOCKER, a molecular dynamics (MD) simulated‐annealing‐based algorithm. A major emphasis in these studies is to compare the relative performance and accuracy of various grid‐based approximations to explicit all‐atom force field calculations. In these docking studies, the protein is kept rigid while the ligands are treated as fully flexible and a final minimization step is used to refine the docked poses. A docking success rate of 74% is observed when an explicit all‐atom representation of the protein (full force field) is used, while a lower accuracy of 66–76% is observed for grid‐based methods. All docking experiments considered a 41‐member protein‐ligand validation set. A significant improvement in accuracy (76 vs. 66%) for the grid‐based docking is achieved if the explicit all‐atom force field is used in a final minimization step to refine the docking poses. Statistical analysis shows that even lower‐accuracy grid‐based energy representations can be effectively used when followed with full force field minimization. The results of these grid‐based protocols are statistically indistinguishable from the detailed atomic dockings and provide up to a sixfold reduction in computation time. For the test case examined here, improving the docking accuracy did not necessarily enhance the ability to estimate binding affinities using the docked structures. © 2003 Wiley Periodicals, Inc. J Comput Chem 13: 1549–1562, 2003
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740
Shea, J.-E., & Brooks III, C. L. (2001) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.52.1.499 · Cited by 481
Cited by
DOCK 6: Impact of new features and current docking performance
Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T., Case, D. A., Kuntz, I. D., & Rizzo, R. C. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23905 · Cited by 833
Combining docking and molecular dynamic simulations in drug design
Alonso, H., Bliznyuk, A. A., & Gready, J. E. (2006) · Medicinal Research Reviews · doi:10.1002/med.20067 · Cited by 759
A perspective on multi‐target drug discovery and design for complex diseases
Ramsay, R. R., Popovic‐Nikolic, M. R., Nikolic, K., Uliassi, E., & Bolognesi, M. L. (2018) · Clinical and Translational Medicine · doi:10.1186/s40169-017-0181-2 · Cited by 718
Receptor–ligand molecular docking
Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014) · Biophysical Reviews · doi:10.1007/s12551-013-0130-2 · Cited by 606
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
Metadata
- Authors
- Wu, G., Robertson, D. H., Brooks, C. L., & Vieth, M.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2003-07-30
- Volume
- 24
- Issue
- 13
- Pages
- 1549-1562
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2156053468
- Cited by
- 1,566
- References
- 2
- FWCI
- 8.74
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm. https://app.cochat.ai/papers/10.1002/jcc.10306
- The paper
- Wu, G., Robertson, D. H., Brooks, C. L., & Vieth, M. (2003). Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm. Journal of Computational Chemistry. https://doi.org/10.1002/jcc.10306
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.