Cited by 554Computational Drug Discovery MethodsReceptor Mechanisms and SignalingCholinesterase and Neurodegenerative Diseases

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A practical guide to large-scale docking

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Bender, Brian J., Gahbauer, Stefan, Luttens, Andreas, Lyu, Jiankun, Webb, Chase M., Stein, Reed M., Fink, Elissa A., Balius, Trent E., Carlsson, Jens, Irwin, John J., Shoichet, Brian K.·2021·Nature Protocols

DOI 10.1038/s41596-021-00597-zPMC PMC8522653

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Abstract

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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UCSF Chimera—A visualization system for exploratory research and analysis
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Method
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Comparative Protein Structure Modeling Using Modeller
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Method

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Madhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R. & Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221–234 (2013). · DOI
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“22,23 Notably, successful structure-based VS provides a protein-ligand complex model, aiding downstream hit evaluation and optimization.”
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“Now, it can perform different tasks during the course of drug discovery, such as hit identification and optimization, prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling [16,17].”
Structural, Thermodynamic, and Dynamic Descriptors for the Differential Mechanism of HIF-2 Activity Modulators
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Metadynamics simulations and site‐directed mutagenesis determine the binding of cannabidiol to the adenosine A 2A receptor
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“In silico methods, those based on physics such as molecular docking (Bender et al. 2021; Sadybekov and Katritch 2023) or those based on artificial intelligence (Abramson et al. 2024), can successfully predict the interaction of ligands with the orthosteric site (Lyu et al. 2019).”
COFFEE-PRESC: A Fast Prescreening Method Using Compound Retrieval by Pairwise Positional Relationship of Representative Fragments
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Additional file 4 of An integrated approach of network pharmacology, molecular docking, and experimental verification uncovers kaempferol as the effective modulator of HSD17B1 for treatment of endometrial cancer
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Additional file 1 of An integrated approach of network pharmacology, molecular docking, and experimental verification uncovers kaempferol as the effective modulator of HSD17B1 for treatment of endometrial cancer
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Additional file 3 of An integrated approach of network pharmacology, molecular docking, and experimental verification uncovers kaempferol as the effective modulator of HSD17B1 for treatment of endometrial cancer
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Additional file 1 of Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation
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Additional file 2 of An integrated approach of network pharmacology, molecular docking, and experimental verification uncovers kaempferol as the effective modulator of HSD17B1 for treatment of endometrial cancer
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Additional file 1 of Systemic evolutionary chemical space exploration for drug discovery
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“For the reliability of docking protocol assessment [57], validation was performed.”
Role of Cheminformatics and Chemometrics in Drug design and Discovery
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Atik, S. B., & Dickson, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.23.701371
“It is now possible to acquire such information through structure-based methods such as docking [6, 24], molecular dynamics (MD) simulations [13], and free-energy calculations.”
The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures
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“However, docking has high computational costs, taking a few minutes to several hours or even days to process one protein pair (Meng et al. 2011, Bender et al. 2021).”
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Ayurvedic-guided Natural Product Discovery as Potential Inhibitors of HIV-1 Proteins
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Self-citation
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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: Bender, Brian J.
Gahbauer, Stefan
Luttens, Andreas
Lyu, Jiankun
Webb, Chase M.
Fink, Elissa A.
Balius, Trent E.
Carlsson, Jens
Irwin, John J.
Shoichet, Brian K.
Venue: Nature Protocols
Publisher: Springer Science and Business Media LLC
Published: 2021-09-24
Volume: 16
Issue: 10
Pages: 4799-4832
ISSN: 1750-2799 (ISSN-L)
1754-2189
DOI: 10.1038/s41596-021-00597-z
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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