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DockOpt: A Tool for Automatic Optimization of Docking Models
Open accessPreprint — published as 10.1021/acs.jcim.3c01406
Abstract
Molecular docking is a widely used technique for leveraging protein structure in ligand discovery, but as a method, it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress towards automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we have developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark dataset, and the generated models for ~84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Knight, I., Tang, K., Mailhot, O., & Irwin, J.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Irwin, John
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-09-11
- Volume
- 64
- Issue
- 3
- Pages
- 1004-1016
- ISSN
- 1549-9596 (ISSN-L)
- PMC
- PMC10865354
- OpenAlex
- W4390755655
- Cited by
- 6
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM133836, GM133836)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- molecular docking, ligand discovery, prospective screening, enrichment analysis
- MeSH terms
- Ligands, Prospective Studies, Protein Binding, Proteins, Molecular Docking Simulation
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