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.
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“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
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