The DOI matches its Crossref record; 145 of the 145 references it cites were found in a registry or an index.
A practical guide to large-scale docking
Open accessAbstract
Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.
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“Within the chemoinformatics space, there are advances in computationally modeling how small molecules and proteins interact via docking.157–159”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [157]
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Cites
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Method
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Cited by
Sorted by citations
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
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BackgroundMethodInfluential
Metadata
- Authors
- Bender, B. J., Gahbauer, S., Luttens, A., Lyu, J., Webb, C. M., Stein, R. M., Fink, E. A., Balius, T. E., Carlsson, J., Irwin, J. J., & Shoichet, B. K.
- Affiliations
- University of California, San Francisco (US)
- Uppsala University (SE)
- Frederick National Laboratory for Cancer Research (US)
- Corresponding author
- Shoichet, Brian K.
- ORCID
- Venue
- Nature Protocols
- Published
- 2021-09-24
- Volume
- 16
- Issue
- 10
- Pages
- 4799-4832
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- PMC
- PMC8522653
- OpenAlex
- W3201539868
- Cited by
- 546
- References
- 145
- Citations by year
- 2026: 96 · 2025: 123 · 2024: 140 · 2023: 117 · 2022: 66 · 2021: 4
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-12-09)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH112205)
- National Institute on Drug Abuse (R37DA045657)
- National Institute of General Medical Sciences (T32GM008284, R01GM133836, F32GM136062, T32GM007175, R35GM122481)
- National Institute of Diabetes and Digestive and Kidney Diseases (U24DK116195)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Health Sciences › Medicine › Pharmacology (medical) › Cholinesterase and Neurodegenerative Diseases
- Keywords
- structure-based virtual screening, binding energy prediction, melatonin receptors, hit identification, cluster computing, drug discovery
- MeSH terms
- Ligands, Software, Drug Design, Drug Discovery, Molecular Docking Simulation
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