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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“To view this ﬁ le in a protein visualization application, they ﬁ rst must be converted to a pdb ﬁ le. csh $DOCKBASE/proteins/showsphere/doshowsph.csh matching_spheres.sph 1 matching_spheres.pdb Input: matching_spheres.sph Output: matching_spheres.pdb The output ﬁ le, matching_spheres.pdb , can be visualized with the r…”
MethodInfluential
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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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New inhibitors of laccase and tyrosinase by examination of cross-inhibition between copper-containing enzymes
Chaudhary, D., Chong, F., Neupane, T., Choi, J., & Jee, J.-G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222413661 · Cited by 14
Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies
Islam, M. A., Dudekula, D. B., Rallabandi, V. P. S., Srinivasan, S., Natarajan, S., Chung, H., & Park, J. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23169374 · Cited by 16
“has become the prototype of structure-based screening of various in-house created chemical libraries or virtually available chemical databases [9].”
BackgroundInfluential
DNA-encoded libraries via late-stage functionalization strategies: a review
Sahu, R., Yadav, S., Nath, S., Banerjee, J., & Kapdi, A. R. (2023) · Chemical Communications · doi:10.1039/d3cc01075a · Cited by 14
A Simple Way to Incorporate Target Structural Information in Molecular Generative Models
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Phytochemical Screening and Antioxidant Potential of Selected Extracts from Betula alba var. pendula Roth., Glycyrrhiza glabra L., and Avena sativa L.
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“The obtained complexes with pubescenoside A and pterisolic acid B were considered valid after optimal poses were obtained, such as when the expected binding pocket was occupied, conformations were not highly strained, and the number of unsatisfied interactions was acceptable [77].”
Background
XDock: A General Docking Method for Modeling Protein–Ligand and Nucleic Acid–Ligand Interactions
Wu, Q., & Huang, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00855 · Cited by 14
Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets
Venkatraman, V., Colligan, T., Lesica, G., Olson, D. R., Gaiser, J., Copeland, C. J., Wheeler, T., & Roy, A. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.874746 · Cited by 12
“Many of these approaches make use of SBVS and facilitate the use of a variety of docking Bender et al. (2021) programs with signi ﬁ cant emphasis on scaling the calculations.”
Method
Dithiocarbazate Ligand‐Based Cu(II), Ni(II), and Zn(II) Complexes: Synthesis, Structural Investigations, Cytotoxicity, DNA Binding, and Molecular Docking Studies
Yusof, E. N. M., Azam, M., Sirat, S. S., Ravoof, T. B. S. A., Page, A. J., Veerakumarasivam, A., Karunakaran, T., & Razali, M. R. (2022) · Bioinorganic Chemistry and Applications · doi:10.1155/2022/2004052 · Cited by 14
“*e AutoDock parameter file was updated to include van der Waals interactions and other 2-specific parameters retrieved from the AutoDock website [42, 43].”
Method
No dance, no partner! A tale of receptor flexibility in docking and virtual screening
Basciu, A., Callea, L., Motta, S., Bonvin, A. M. J. J., Bonati, L., & Vargiu, A. V. (2022) · Annual reports in medicinal chemistry · doi:10.1016/bs.armc.2022.08.006 · Cited by 14
Virtual screening and zebrafish models in tandem, for drug discovery and development
Hernández-Silva, D., Alcaraz‐Pérez, F., Pérez‐Sánchez, H., & Cayuela, M. L. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2147503 · Cited by 12
In Silico Drug Repurposing Framework Predicts Repaglinide, Agomelatine and Protokylol as TRPV1 Modulators with Analgesic Activity
Andrei, C., Mihai, D. P., Zanfirescu, A., Nițulescu, G. M., & Negreș, S. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14122563 · Cited by 13
“As observed in many drug discovery campaigns, docking scores alone do not often yield remarkably high success rates in discovering true positives [53] or appropriate repurposing candidates.”
BackgroundMethod
Multitargeted molecular docking and dynamics simulation studies of flavonoids and volatile components from the peel of Citrus sinensis L. (Osbeck) against specific tumor protein markers
Gandhi, G. R., Sharanya, C. S., Jayanandan, A., Haridas, M., Hillary, V. E., Gandhi, S. R., Sridharan, G., RENGARAJU, S., Vasconcelos, A. B. S., Montalvão, M. M., et al. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2212062 · Cited by 13
“In this setting, higher log AUC values correspond to better discrimination between active and inactive molecules (actives and inactive) and provide a sanity check on the ability of the docking parameters to identify actives (Bender et al., 2021).”
Background
Novel Polystyrene-Binding Nanobody for Enhancing Immunoassays: Insights into Affinity, Immobilization, and Application Potential
Deng, Y., Liu, J., Lu, Y., Fan, X., Yang, Y., Xu, Y., Qin, X., Nian, R., & Liu, W. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c04375 · Cited by 13
PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model
Smith, L. G., Novak, B., Osato, M., Mobley, D. L., & Bowman, G. R. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00870 · Cited by 15
Technologies for the discovery of G protein–coupled receptor–targeting biologics
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Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer’s Disease Cohorts
Wang, H., Li, J., Tu, W., Wang, Z., Zhang, Y., Chang, L., Wu, Y., & Zhang, X. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-240301 · Cited by 14
Toward Unveiling Putative Binding Sites of Interleukin-33: Insights from Mixed-Solvent Molecular Dynamics Simulations of the Interleukin-1 Family
Mai, T. T., Lam, T.-P., Pham, L.-H. D., Nguyen, K.-H., Nguyen, Q., Le, M.-T., & Thai, K. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c03057 · Cited by 14
Ten challenges and opportunities in computational immuno-oncology
Bao, R., Hutson, A. D., Madabhushi, A., Jönsson, V. D., Rosario, S. R., Barnholtz‐Sloan, J. S., Fertig, E. J., Marathe, H. G., Harris, L. N., Altreuter, J., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-009721 · Cited by 14
“27 28 For large-scale phase III randomized trials, the design must consider delays in effects relative to”
Barlow Twins deep neural network for advanced 1D drug–target interaction prediction
Schuh, M. G., Boldini, D., Bohne, A. I., & Sieber, S. A. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00952-2 · Cited by 13
“1, 2 and 7.”
De novo in silico screening of natural products for antidiabetic drug discovery: ADMET profiling, molecular docking, and molecular dynamics simulations
Ibrahim, S. O., Ayipo, Y. O., Lukman, H. Y., Abubakar, F. A., Na’Allah, A., Katibi-Abdullahi, R. A., Zubair, M. F., & Atolanı, O. (2025) · In Silico Pharmacology · doi:10.1007/s40203-025-00320-w · Cited by 14
“Validating the accuracy of molecular docking protocols is essential to ensure the reliability of predicted binding poses and interaction strengths (Bender et al. 2021 ).”
Structure-based discovery of positive allosteric modulators of the A 1 adenosine receptor
Nguyen, A. T. N., Panel, N., Vo, D. D., Thai, B. S., Chia, L. Y., Lu, C. S., Hellyer, S. D., Langiu, M., Jörg, M., Gregory, K. J., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2421687122 · Cited by 13
Self-citation
TextGrad: Automatic "Differentiation" via Text
Yuksekgonul, M., Bianchi, F., Boen, J., Liu, S., Huang, Z., Guestrin, C., & Zou, J. (2024) · arXiv · doi:10.48550/arxiv.2406.07496 · Cited by 12
“In particular, docking scores tend to prefer larger molecules with many functional groups that maximize interactions with a binding site [55, 56, 60].”
Background
Quantum-machine-assisted drug discovery
Zhou, Y., Chen, J., Cheng, J., Cao, X., Zhang, Y., Karemore, G., Žitnik, M., Chong, F. T., Liu, J., Fu, T., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-025-00033-2 · Cited by 27
“CPU/GPU: seconds–minutes/complex; MLdockingoften minutes;AF3 minutes/complex;HPC forlarge cross-docking[76,81, 82,86,201] Not yet feasible for protein-scale interactions; Requiresthousands ofqubits[36] 10 4 -10 5 logical qubits; Days runtime; Full quantum simulation of binding dynamics [33, 202]…”
Ultra-large virtual screening unveils potent agonists of the neuromodulatory orphan receptor GPR139
de Vaca, I. C., Trapkov, B., Shen, L., Vo, D. D., Zhang, X., Yang, Y., Pezeshki, M., Zhang, X., Bällgren, F., Saleh, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66845-y · Cited by 15
Self-citation
Structure-Based Discovery of Receptor Activator of Nuclear Factor-κB Ligand (RANKL)-Induced Osteoclastogenesis Inhibitors
Rinotas, V. E., Liepouri, F., Ouzouni, M., Chalkidi, N., Papaneophytou, C. P., Lampropoulou, M., Vidali, V. P., Kontopidis, G. A., Couladouros, E. A., Eliopoulos, E., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241411290 · Cited by 12
One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse
Malatesta, M., Fornasier, E., di Salvo, M. L., Tramonti, A., Zangelmi, E., Peracchi, A., Secchi, A., Polverini, E., Giachin, G., Battistutta, R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47466-3 · Cited by 13
“This technique is successfully used for large-scale identi ﬁ cation of potential drugs 12,13 .”
Synthesis and cytotoxic evaluation of heterocyclic compounds by vinylic substitution of ketene dithioacetals
Baliza, L. R. S. P., Freitas, T. R., Gonçalves, E. K. S., Antunes, G. R., Souza, A. J. F., Yoneda, J. D., Duarte, C. L., Andrade, S. N., Sabino, A. D. P., Varotti, F. D. P., et al. (2024) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14581 · Cited by 12
In vivo determination of analgesic and anti-inflammatory activities of isolated compounds from Cleome amblyocarpa and molecular modelling for the top active investigated compounds
El-Ayouty, M. M., Eltahawy, N. A., El-sameaa, A. M. A., Badawy, A. M., Darwish, K. M., Elhady, S. S., Shokr, M. M., & Ahmed, S. A. (2024) · RSC Advances · doi:10.1039/d4ra04496g · Cited by 12
Exploration of alpha-glucosidase inhibitors: A comprehensive in silico approach targeting a large set of triazole derivatives
Abchır, O., Khedraoui, M., Yamari, I., Nour, H., Errougui, A., Samadi, A., & Chtita, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308308 · Cited by 12
“In this study, computational methods were employed to forecast the binding affinity and activity of triazole derivatives against the target protein, along with their potential as oral medications [27–29].”
Method
SpaceHASTEN: A Structure-Based Virtual Screening Tool for Nonenumerated Virtual Chemical Libraries
Kalliokoski, T., Turku, A., & Käsnänen, H. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01790 · Cited by 13
Exploring novel and potent glycogen synthase kinase-3β inhibitors through systematic drug designing approach
Ganai, S. A., Suma Mohan, S., & Padder, S. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-85868-5 · Cited by 14
Efficient Screening of Target‐Specific Selected Compounds in Mixtures by 19F NMR Binding Assay with Predicted 19F NMR Chemical Shifts
Vulpetti, A., Lingel, A., Dalvit, C., Schiering, N., Oberer, L., Henry, C., & Lu, Y. (2022) · ChemMedChem · doi:10.1002/cmdc.202200163 · Cited by 12
Maximizing the integration of virtual and experimental screening in hit discovery
Bajusz, D., & Keserű, G. M. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2085685 · Cited by 11
“Guidelines for large-scale docking based on the DOCK software and the ZINC database were recently published as well [38].”
Method
Library size in virtual screening: is it truly a number’s game?
Kontoyianni, M. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2130244 · Cited by 13
Development of an automated screen for Kv7. 2 potassium channels and discovery of a new agonist chemotype
Hernandez, C. C., Tarfa, R. A., Miguel I. Limcaoco, J., Liu, R., Mondal, P., Hill, C., Duncan, K. R., Tzounopoulos, T., Stephenson, C. R. J., O'Meara, M. J., et al. (2022) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2022.128841 · Cited by 11
Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities
Azam, F., & Almahmoud, S. A. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27073302 · Cited by 13
Preliminary in Silico Studies of the Interactions of Certain Genotoxic Azo Dyes with Different Double-Stranded DNA Conformations
Istıflı, E. S. (2022) · Colorants · doi:10.3390/colorants1020015 · Cited by 11
“success of docking experiments; control molecules are useful in distinguishing real active molecules from real inactive molecules [35].”
Background
Spermine and spermidine bind CXCR4 and inhibit CXCR4- but not CCR5-tropic HIV-1 infection
Harms, M., Smith, N., Han, M., Groß, R. M., von Maltitz, P., Stürzel, C. M., Ruiz‐Blanco, Y. B., Almeida‐Hernández, Y., Rodríguez, A., Cathelin, D., et al. (2023) · Science Advances · doi:10.1126/sciadv.adf8251 · Cited by 11
Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential
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Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures
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In silico Profiling and Pharmacokinetic Modelling of Olivetol: Evaluating its Potential as a Therapeutic Agent for Diabetic Wound Healing
Nirenjen, S., & Jaishankar, N. (2024) · Current Drug Discovery Technologies · doi:10.2174/0115701638332872240922184903 · Cited by 11
Computational profiling and pharmacokinetic modelling of Febuxostat: Evaluating its potential as a therapeutic agent for diabetic wound healing
Nirenjen, S., & Narayanan, J. (2024) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2024.130735 · Cited by 11
Tripeptides inhibit dual targets AChE and BACE-1: a computational study
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Using In Silico Molecular Docking to Explain Differences in Receptor Binding Behavior of HHC and THCV Isomers: Revealing New Binding Modes
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Docking 14 Million Virtual Isoquinuclidines against the μ and κ Opioid Receptors Reveals Dual Antagonists–Inverse Agonists with Reduced Withdrawal Effects
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Self-citation
Identification of novel potential hepatitis E virus inhibitors as seen from molecular docking, free energy landscape and molecular dynamics simulation studies
Moharana, M., Pattanayak, S. K., & Khan, F. (2023) · Molecular Simulation · doi:10.1080/08927022.2023.2202764 · Cited by 10

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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