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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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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BackgroundMethod
In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review
Srivastava, M., Singh, K., Kumar, S., Hasan, S. M., Mujeeb, S., Kushwaha, S. P., & Husen, A. (2024) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/0113895575287322240115115125 · Cited by 5
In-silico and in-vitro studies to identify potential inhibitors of SARS-CoV-2 spike protein from Omani medicinal plants
Al-Mahrami, N., Nair, S. S. K., Al‐Mawali, A., Shafreen, R. B., Ullah, S. M., Halim, S. A., Al‐Harrasi, A., & Sivakumar, N. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39649 · Cited by 5
Natural Products with Potential for the Treatment of Pain: Global Evidence from the NAPRALERT Database
Graham, J. G., Bisson, J. I., Harris, G. H., Wang, Z. J., Waller, D. P., & Pauli, G. F. (2024) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c00439 · Cited by 5
Large Library Docking and Biophysical Analysis of Small-Molecule TMPRSS2 Inhibitors
Fraser, B. J., Young, N. J., Bender, B. J., Gahbauer, S., Ilyassov, O., Wilson, R. P., Li, Y., Seitova, A., Lourenço, A. L., Chung, D. H., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c03089 · Cited by 5
Self-citation
Cache: Utilizing ultra-large library screening in Rosetta to identify novel binders of the WD-repeat domain of Leucine-Rich Repeat Kinase 2
Ließmann, F., Eisenhuth, P., Fürll, A., Vu, O., Moretti, R., & Meiler, J. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01084-3 · Cited by 7
“In recent years, there has been an explosion of computer-aided screening methods, driven by the integration of advanced computational techniques, artificial intelligence (AI), and machine learning (ML) [3–6].”
Mechanistic insights into the antidiarrheal action of phlorizin: modulation of TLR4/NF-κB, PI3K/AKT, and HIF-1 signalling with antispasmodic and antimicrobial effects
Kushwah, R., Jain, S., Sahu, M., Chinchu Praisthy, L. J., Maurya, S., Ansari, T., Mansi, Mishra, A. K., & Kuznetsov, A. (2025) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-025-04814-4 · Cited by 5
Combining In Vitro and In Silico New Approach Methods to Investigate Type 3 Iodothyronine Deiodinase Chemical Inhibition Across Species
Mayasich, S. A., Goldsmith, M. R., Mattingly, K. Z., & LaLone, C. A. (2023) · Environmental Toxicology and Chemistry · doi:10.1002/etc.5591 · Cited by 5
“…targets with holo crystal structures (with bound ligands) than apo structures like that for the DIO3, and virtual screening may have greater utility in eliminating inactive (nonbinding) compounds from further consideration than in ranking between inhibitors of similar potency (Bender et al., 2021).”
Background
Structure-based virtual screening, molecular simulation and free energy calculations of traditional Chinese medicine, ZINC database revealed potent inhibitors of estrogen-receptor α (ERα)
Shahab, M., Zulfat, M., & Zheng, G. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2275174 · Cited by 5
“To determine the optimum binding configuration, these configurations have been assessed using scoring methods (Bender et al., 2021; McNutt et al., 2021).”
Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists
Tran, X.-T. D., Phan, T.-L., To, V.-T., Tran, N.-V. N., Nguyen, N.-N. S., Nguyen, D.-N. H., Tran, N.-T. N., & Truong, T. N. (2024) · Frontiers in Chemistry · doi:10.3389/fchem.2024.1382319 · Cited by 5
Antitumor effect of a new human epidermal growth factor receptor inhibitor
Kit, O. I., Kodonidi, I. P., Frantsiyants, E. M., Kaplieva, I. V., Glushko, A. A., Trepitaki, L. K., Surikova, E. I., Bandovkina, V. A., Pogorelova, Y. A., Neskubina, I. V., et al. (2024) · Research and Practical Medicine Journal · doi:10.17709/2410-1893-2024-11-3-4 · Cited by 5
Computational design of potent dimeric phenylthiazole NS5A inhibitors for hepatitis C virus
Liman, W., Oubahmane, M., Lahcen, N. A., Hdoufane, I., Cherqaoui, D., Daoud, R., & Allali, A. E. (2024) · Scientific Reports · doi:10.1038/s41598-024-80082-1 · Cited by 5
Discovery of novel anti-tumor compounds targeting PARP-1 with induction of autophagy through in silico and in vitro screening
Shi, D., Pang, Q., Qin, Q., Yao, X., Yao, X., & Yu, Y. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1026306 · Cited by 5
“Since the high false positive rate is always a big problem in virtual screening (Adeshina et al., 2020; Bender et al., 2021), other strategies were also designed to ensure the reliability of virtual screening during the whole process, including the evaluation of docking and screening abilities, structural diversity an…”
Background
Integration of molecular modelling and in vitro studies to inhibit LexA proteolysis
Schuurs, Z. P., McDonald, J. P., Croft, L. V., Richard, D. J., Woodgate, R., & Gandhi, N. S. (2023) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2023.1051602 · Cited by 6
“These approaches have also facilitated the ability to screen large chemical spaces in a relatively short amount of time and at little cost compared to their counterpart in vitro screening techniques (Bender et al., 2021).”
Background
Computational insights into allosteric inhibition of focal adhesion kinase: A combined pharmacophore modeling and molecular dynamics approach
Kumar, V., Singh, P., Parate, S., Singh, R., Ro, H., Song, K. S., Lee, K. W., & Park, Y. (2024) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2024.108789 · Cited by 5
CBD Versus CBDP: Comparing In Vitro Receptor-Binding Activities
Haghdoost, M., Young, S., Holloway, A. K., Roberts, M., Zvorsky, I. A., & Bonn‐Miller, M. O. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25147724 · Cited by 5
3D physiologically-informed deep learning for drug discovery of a novel vascular endothelial growth factor receptor-2 (VEGFR2)
Xu, M., Xiao, X., Chen, Y., Zhou, X., Parisi, L., & Ma, R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35769 · Cited by 5
DINC-ensemble: A web server for docking large ligands incrementally to an ensemble of receptor conformations
Conev, A., Chen, J., & Kavraki, L. E. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169163 · Cited by 5
“Molecular docking is a common computational approach for modeling protein-ligand interactions and is used to assist structure-based drug discovery [1].”
warpDOCK: Large-Scale Virtual Drug Discovery Using Cloud Infrastructure
McDougal, D. P., Rajapaksha, H., Pederick, J. L., & Bruning, J. B. (2023) · ACS Omega · doi:10.1021/acsomega.3c02249 · Cited by 6
Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs
Li, J., Zhao, K., Yang, G., Lv, H., Zhang, R., Li, S., Chen, Z., Xu, M., Yang, N., & Dai, S. (2025) · Molecules · doi:10.3390/molecules30122653 · Cited by 4
“To address this, molecular docking is planned to explore potential drug targets [77], although detailed experimental validation will be required for more reliable insights.”
Locusts adopt IP 3 as a second messenger for olfactory signal transduction
Yang, J., He, H., Dong, S., Lv, J., Cheng, L., Yu, Q., Kang, L., & Guo, X. (2025) · Science Advances · doi:10.1126/sciadv.ads1352 · Cited by 4
Drug repositioning pipeline integrating community analysis in drug-drug similarity networks and automated ATC community labeling to foster molecular docking analysis
Colibăşanu, D., Groza, V., Occhiuzzi, M. A., Grande, F., Udrescu, M., & Udrescu, L. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1666716 · Cited by 4
“However, molecular docking requires significant computational resources, and applying it on a large scale is cumbersome Bender et al. (2021).”
Stress-Induced Plant Specialized Metabolism: Signaling, Multi-Omics Integration, and Plant-Derived Antimicrobial Metabolites to Combat Antimicrobial Resistance
Pérez-Sánchez, L. E., Ayala-Guerrero, L. M., Mendieta‐Moctezuma, A., Villalobos-López, M. Á., & Ríos-Meléndez, S. (2026) · Plants · doi:10.3390/plants15020193 · Cited by 5
“Crucially, they are also used to estimate ADMET profiles (Absorption, Distribution, Metabolism, Excretion, Toxicity), which are fundamental for prioritization and do not replace bioassays [70].”
Targeting epigenetic regulators: In-silico discovery of natural inhibitors against histone demethylase KDM4C
Kumar, M., Anusha, P., Mukhopadhyay, S., Chowdhury, S., Phalak, M., Devi, U., & Shukla, P. (2026) · PLoS ONE · doi:10.1371/journal.pone.0340107 · Cited by 4
“While molecular docking offers a useful initial screening of protein–ligand interactions, it has inherent limitations due to simplified scoring functions and static models [54].”
VsNsbench: evaluating AlphaFold3-embed induced-fit mechanism for enhanced virtual screening
Gu, S., Shen, C. Q., Yang, Y., Zhai, S.-L., Li, J., Tian, Y.-N., Zhang, X., Du, H., Wu, Z., Wang, X., et al. (2026) · Acta Pharmacologica Sinica · doi:10.1038/s41401-025-01732-2 · Cited by 4
“Notably, the choice of protein structure signi ﬁ cantly in ﬂ uences screening ef ﬁ cacy, as evidenced by the superior enrichment capability of experimentally determined holo structures (bound to speci ﬁ c ligands) compared to their apo -form counterparts (ligand free) [4, 5].”
Exploring the Structure–Activity Relationships and Molecular Mechanisms of Black Soldier Fly-Derived Antimicrobial Peptides with AI Insights
Tariq, M. R., Wang, H., Liu, S., Armenia, I., Tettamanti, G., Korai, S. K., Lin, H., Zheng, C., Liang, Y., Qin, J., et al. (2026) · Insects · doi:10.3390/insects17020207 · Cited by 4
Integrating Hydrogen Exchange with Molecular Dynamics for Improved Ligand Binding Predictions
Walters, B. T., Patapoff, A. W., Kiefer, J. R., Wu, P., & Wang, W. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00397 · Cited by 4
Leveraging computer-aided drug design for the discovery of phytohormone analogs: A review
Guo, Y., Qanmber, G., Liu, Z., Yang, H., Li, J., Yang, Z., Li, Z., Yang, L., & Yang, L. (2025) · Plant Stress · doi:10.1016/j.stress.2025.100951 · Cited by 4
Computational profiling of terpenoids for putative dual-target leads against Staphylococcus aureus penicillin binding protein 2a and beta-lactamase: An integrated structure-activity-relationship-based study
Gyebi, G. A., & Sabiu, S. (2025) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2025.128617 · Cited by 4
Visualization of tumor-associated neutrophils in malignant ascites of ovarian cancer with aptamer-decorated AIE probes
Wang, J., Fang, X., Yang, J., Tang, Q., Yang, Y., Li, Z., Zhang, F., Wu, Q., Luo, Z., & Zheng, Z. (2024) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2024.135385 · Cited by 4
Differential senolytic inhibition of normal versus Aβ-associated cholinesterases: implications in aging and Alzheimer’s disease
Darvesh, S., Cash, M. K., Forrestall, K. L., Maillet, H., & Sands, D. C. (2025) · Aging · doi:10.18632/aging.206227 · Cited by 4
“Crystal structures for human AChE (Protein Databank code (PDB): 4M0E, 2.00 Å resolution) [91] and BChE (PDB: 4TPK, 2.70 Å) [92] were obtained from the Protein Databank [146] and were selected based on resolution and co-crystallization properties deemed appropriate for molecular docking [100].”
Influential
Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water
Mteremko, D., Chilongola, J. O., Paluch, A. S., & Chacha, M. N. (2022) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2022.108348 · Cited by 4
Strategies for Competitive Activity‐Based Protein Profiling in Small Molecule Inhibitor Discovery and Characterization
Zhu, H., Sharafi, M., Teh, W. P., Bratt, A. S., Buhrlage, S. J., & Marto, J. A. (2023) · Israel Journal of Chemistry · doi:10.1002/ijch.202200113 · Cited by 4
Allosteric modulators of solute carrier function: a theoretical framework
Boytsov, D., Schicker, K. W., Hellsberg, E., Freissmuth, M., & Sandtner, W. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1166450 · Cited by 4
“, several G protein-coupled receptors (Orry et al., 2006; Kolb et al., 2009; Kolb and Irwin, 2009; Katritch et al., 2011; Rognan, 2011; Gabrielsen et al., 2012; Kufareva et al., 2012; Gabrielsen et al., 2013; Ngo et al., 2016; Rognan, 2017; Roth et al., 2017; Scharf et al., 2019; Alon et al., 2021; Ballante et al., 20…”
Background
Licraside as novel potent FXR agonist for relieving cholestasis: structure-based drug discovery and biological evaluation studies
Xi, L., Shi, A., Shen, T., Wang, G., Wei, Y., & Guo, J. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1197856 · Cited by 4
Achieving the promise and avoiding the peril of chemical probes using genetics
Jones, N. H., & Kapoor, T. M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102628 · Cited by 4
Molecular Docking in Drug Designing and Metabolism
Saikia, S., Puzari, M., & Chetia, P. (2023) · Springer eBooks · doi:10.1007/978-981-99-2816-3_14 · Cited by 4
CHEIC: Chemical Image Classificator. An intelligent system for identification of volatiles compounds with potential for respiratory diseases using Deep Learning
Vieira, R., de Sousa, K. A., da Silva, G. S., Silva, D. H. S., & Castro‐Gamboa, I. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121178 · Cited by 4
Understanding and Predicting Ligand Efficacy in the μ-Opioid Receptor through Quantitative Dynamical Analysis of Complex Structures
Galdino, G. T., Mailhot, O., & Najmanovich, R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00788 · Cited by 3
Structure-Based Discovery of a NPFF1R Antagonist with Analgesic ActivityPreprint
Bender, B. J., Pickett, J. E., Bráz, J. M., Kang, H. J., Gahbauer, S., Bhardwaj, K., Rodriguez-Rosado, S., Liu, Y., Jain, M. K., Basbaum, A. I., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.25.564029 · Cited by 5
“A final set of well-performing models was selected for a screen with the known ligands embedded in a library of about 1 million monocations selected from the larger library 14,27 .”
BackgroundMethodInfluentialSelf-citation
Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approachPreprint
Wills, S., Sánchez-García, R., Roughley, S. D., Merritt, A. T., Hubbard, R. E., von Delft, F., & Deane, C. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.02.606367 · Cited by 4
“Thus, early exploration of chemical space is important, enabling a comprehensive understanding of the structure–activity relationship (SAR) and of which substructures are required for (or will ablate) binding [20, 21].”
Background
Correlation of protein binding pocket properties with hits’ chemistries used in generation of ultra-large virtual libraries
Song, R. X.-D., Nicklaus, M. C., & Tarasova, N. I. (2024) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-024-00562-4 · Cited by 3
“Screening of virtual libraries of synthesizable compounds has become an increasingly important step in drug discovery [1, 2].”
Background
Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation
Lee, D., Hwang, W., Byun, J., & Shin, B. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05923-2 · Cited by 3

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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A practical guide to large-scale docking

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