The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
DOCK 6: Impact of new features and current docking performance
Open accessAbstract
This manuscript presents the latest algorithmic and methodological developments to the structure‐based design program DOCK 6.7 focused on an updated internal energy function, new anchor selection control, enhanced minimization options, a footprint similarity scoring function, a symmetry‐corrected root‐mean‐square deviation algorithm, a database filter, and docking forensic tools. An important strategy during development involved use of three orthogonal metrics for assessment and validation: pose reproduction over a large database of 1043 protein‐ligand complexes (SB2012 test set), cross‐docking to 24 drug‐target protein families, and database enrichment using large active and decoy datasets (Directory of Useful Decoys [DUD]‐E test set) for five important proteins including HIV protease and IGF‐1R. Relative to earlier versions, a key outcome of the work is a significant increase in pose reproduction success in going from DOCK 4.0.2 (51.4%) → 5.4 (65.2%) → 6.7 (73.3%) as a result of significant decreases in failure arising from both sampling 24.1% → 13.6% → 9.1% and scoring 24.4% → 21.1% → 17.5%. Companion cross‐docking and enrichment studies with the new version highlight other strengths and remaining areas for improvement, especially for systems containing metal ions. The source code for DOCK 6.7 is available for download and free for academic users at http://dock.compbio.ucsf.edu/. © 2015 Wiley Periodicals, Inc.
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
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
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
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535
Self-citationZINC − 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
Current Status of the AMOEBA Polarizable Force Field
Ponder, J. W., Wu, C., Ren, P., Pande, V. S., Chodera, J. D., Schnieders, M. J., Haque, I., Mobley, D. L., Lambrecht, D. S., DiStasio, R. A., et al. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp910674d · Cited by 1,440
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
Yun, C.-H., Boggon, T. J., Li, Y., Woo, M. S., Greulich, H., Meyerson, M., & Eck, M. J. (2007) · Cancer Cell · doi:10.1016/j.ccr.2006.12.017 · Cited by 1,192
Predicting Absolute Ligand Binding Free Energies to a Simple Model Site
Mobley, D. L., Graves, A. P., Chodera, J. D., McReynolds, A. C., Shoichet, B. K., & Dill, K. A. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.06.002 · Cited by 310
Cited by
Software for molecular docking: a review
Pagadala, N. S., Syed, K., & Tuszynski, J. (2017) · Biophysical Reviews · doi:10.1007/s12551-016-0247-1 · Cited by 1,522
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
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211081 · Cited by 284
“These assessments of model performance indicate that certain machine learning-based scoring functions improve prediction accuracy.”
BackgroundMethodInfluentialReliable and Accurate Solution to the Induced Fit Docking Problem for Protein–Ligand Binding
Miller, E. B., Murphy, R. B., Sindhikara, D., Borrelli, K. W., Grisewood, M. J., Ranalli, F., Dixon, S. L., Jerome, S., Boyles, N. A., Day, T., et al. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00136 · Cited by 210
Metadata
- Authors
- 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.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2015-04-27
- Volume
- 36
- Issue
- 15
- Pages
- 1132-1156
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2042572511
- Cited by
- 833
- References
- 14
- FWCI
- 47.91
- Type
- journal article
- Language
- en
- Open access
- green
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: DOCK 6: Impact of new features and current docking performance. https://app.cochat.ai/papers/10.1002/jcc.23905
- The paper
- 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). DOCK 6: Impact of new features and current docking performance. Journal of Computational Chemistry. https://doi.org/10.1002/jcc.23905
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.
