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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MethodInfluential
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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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ВИКОРИСТАННЯ МЕТОДІВ ДОСЛІДЖЕНЬ IN SILICO ДЛЯ ПРОГНОЗУВАННЯ ФАРМАКОКІНЕТИЧНИХ ВЛАСТИВОСТЕЙ ТА ПОШУКУ БІОЛОГІЧНО АКТИВНИХ РЕЧОВИН
Zahrychuk, O. H., Matyashchuk, U. O., Korjovska, V. V., Мілян, І. І., Poliovyi, D. O., Zahrychuk, H. Y., & Demyd, A. Y. (2024) · Фармацевтичний часопис · doi:10.11603/2312-0967.2024.3.14868 · Cited by 2
A Novel Approach for Apical Periodontitis Prevention Through RIPK3 Inhibition Using Organic Flavonoids
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“Compared to conventional experimental techniques, in silico screening offers a rapid and cost-effective approach to drug discovery (13).”
Background
Amide Functionalized Novel Pyrrolo-pyrimidine Derivative as Anticancer Agents: Synthesis, Characterization and Molecular Docking Studies
Bandaru, P. K., Nidasanametla, S. K. R., & Shyamala, P. (2024) · Anti-Cancer Agents in Medicinal Chemistry · doi:10.2174/0118715206333935241004070350 · Cited by 2
Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues
Grassmann, G., Rienzo, L. D., Ruocco, G., Miotto, M., & Milanetti, E. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02286 · Cited by 2
Ultra-Large-Scale Virtual Screening
Pöhner, I., Sivula, T., & Poso, A. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_11 · Cited by 2
M01 tool: an automated, comprehensive computational tool for generating small molecule-peptide hybrids and docking them into curated protein structures
Sheikholeslami, M. N., Nazari, M., & Fassihi, A. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06120-5 · Cited by 2
“Although automated protonation applications are not optimal [25], their use in high-throughput practices is widely accepted [26].”
Understanding the HIV-CA protein and the ligands that bind at the N-terminal domain (NTD) - C-terminal domain (CTD) interface
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Identification of chemical scaffolds for targeting ubiquitin-specific protease 11 (USP11) through high-throughput virtual screening
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“…the enrichment factor (eF) to assess each model’s ability to discriminate binders from decoys would have strengthened benchmarking confidence 30 . however, with only one known inhibitor available, our primary focus was to identify a model that ranked mitoxantrone as the highest-scoring…”
Panaxadiol acts as an HIF-1α inhibitor to suppress H9N2-induced inflammation
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Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis
Kenny, S. E., Antaw, F., Locke, W. J., Howard, C. B., Korbie, D. J., & Trau, M. (2022) · Life · doi:10.3390/life12030363 · Cited by 2
“Although chemical compound structures are more easily computationally predicted than proteins, molecular docking still requires the input of a high-resolution image of the target structure (preferably ligand-bound) [81].”
BackgroundInfluential
Towards an Enrichment Optimization Algorithm (EOA)‐based Target Specific Docking Functions for Virtual Screening
Spiegel, J. D., & Senderowitz, H. (2022) · Molecular Informatics · doi:10.1002/minf.202200034 · Cited by 2
In Silico Study for Acyclovir and Its Derivatives Against Mpro of nCoV: Temperature Dependent Molecular Dynamics SimulationsPreprint
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Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
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Structure-based discovery of inhibitors of the SARS-CoV-2 Nsp14 N7 -methyltransferasePreprint
Singh, I., Li, F., Fink, E. A., Chau, I., Li, A., Rodríguez‐Hernández, A., Glenn, I., Belinchón, F. J. Z., Rodríguez, M. R., Devkota, K., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.12.523677 · Cited by 2
Self-citation
A perspective on the sharing of docking data
Aci‐Sèche, S., Bourg, S., Bonnet, P., Rebehmed, J., de Brevern, A. G., & Diharce, J. (2023) · Data in Brief · doi:10.1016/j.dib.2023.109386 · Cited by 2
Enhancing Ligand Pose Sampling for Molecular DockingPreprint
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“In practical scenarios, both flexible protein docking and deep-learning-based approaches consistently demonstrate lower accuracy compared to rigid protein docking [Ravindranath et al., 2015, Bender et al., 2021, Krishna et al., 2023].”
Method
One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.Preprint
Wellnitz, J., Jain, S., Hochuli, J. E., Maxfield, T., Muratov, E. N., Tropsha, A., & Zakharov, A. V. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-9cw9v · Cited by 2
Cambridge Structural Database ( CSD ) – Drug Discovery Through Data Mining & Knowledge‐Based Tools
Stanzione, F., Chikhale, R. V., & Friggeri, L. (2024) · doi:10.1002/9783527840748.ch18 · Cited by 2
Structure‐Based Ultra‐Large Virtual Screenings
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Condensing Molecular Docking CNNs via Knowledge DistillationPreprint
McNutt, A. T., Li, Y., Francoeur, P. G., & Koes, D. R. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-0jh8g · Cited by 1
Benchmarking Real-World Applicability of Molecular Generative Models from De novo Design to Lead Optimization with MolGenBenchPreprint
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Innovative Integration of Butina Clustering with Ensemble Learning Techniques for the Refined Pharmacophore Modeling of Apelin Receptor Agonists: A High-Impact Computational ApproachPreprint
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Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results
Hansel-Harris, A., Santos‐Martins, D., Bruciaferri, N., Tillack, A. F., Holcomb, M., & Forli, S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00166 · Cited by 1
Development of Purinergic Receptor Agonists and Antagonists
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In-silico Docking and Dynamics Simulation Analysis of Peroxisome Proliferator-Activated Receptor-Gamma and β-Carotene
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Rational Design of Drugs Targeting G-Protein-Coupled Receptors: Ligand Search and Screening
Luginina, A. P., Khnykin, A. N., Khorn, P. A., Moiseeva, O. V., Safronova, N. A., Pospelov, V. A., Dashevskii, D. E., Belousov, A., Borschevskiy, V. I., & Mishin, A. (2024) · Biochemistry (Moscow) · doi:10.1134/s0006297924050158 · Cited by 1
Quantum Computing
Abhishek, A., Bhatt, P., Baig, M. N., Sridevi, R., & Hazela, B. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3212-2.ch007 · Cited by 3
Effect of Atomic Size and Stereochemistry of Halogenated Hydantoin Derivatives on Main Protease Inhibition Activities of SARS-CoV-2: Rationalization Through Computational Studies
Pal, A., & Chordia, M. D. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2024.3515952 · Cited by 1
Small Molecules Identified by an In Silico Docking Screen Targeting Anaphase-Promoting Complex/Cyclosome Subunit 1 (APC1) Potentiate Paclitaxel-Induced Breast Cancer Cell Death
Schuyler, S. C., Gupta, R., Nguyen, T. T. B., Weng, C.-Y., & Chen, H.-Y. (2025) · Molecules · doi:10.3390/molecules30040895 · Cited by 1
“AutoDock Vina (version 1.2.4), released in May 2024 (The Scripps Institute, University of California at San Diego, CA, USA), was used for the docking simulations, and 15,000 molecules have been screened [23–25].”
Influential
Structure-Based Identification of Non-covalent Prolyl Oligopeptidase 80 Inhibitors Targeting Trypanosoma cruzi Cell Entry
Costa, V. A. F., Motta, F. N., Carvalho, A. M. D. S., de Melo, F. D. S. M., Mottin, M., Charneau, S. O., Grellier, P., Santana, J. M., Bastos, I. M. D., & Neves, B. J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02152 · Cited by 2
Identifying Potent Compounds Using Pairwise Consensus Methods
Xu, M., Wu, C., Wang, S., Zhan, W., Guo, L., Li, Y., Vogel, H., & Yuan, S. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00942 · Cited by 1
Novel 5-((Phenylimino)methyl)-1,2,4-triazol-3-one Derivatives: Synthesis, Anticancer Potential and Molecular Docking Insights
Balavanthapu, R., & Vedula, G. S. (2025) · Asian Journal of Chemistry · doi:10.14233/ajchem.2025.33249 · Cited by 1
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Aljohani, H. M., Bokhari, B. T., Saleh, A. M., Alyahyawi, A. Y., Alhamawi, R. M., Jaddah, M. M., Alobaidy, M. A., & Eisa, A. A. (2025) · Current Issues in Molecular Biology · doi:10.3390/cimb47080658 · Cited by 1
“Another parameter of evaluation was the enrichment of the ligand interactions network at the active pocket [31].”
Thymol as a Potential Natural Antiemetic: Insights From In Vivo and In Silico Studies
Akbor, M. S., Bappi, M. H., Prottay, A. A. S., Haque, F., Mia, N., Islam, T., Almarhoon, Z. M., Gürer, E. S., Setzer, W. N., Sharifi‐Rad, J., et al. (2025) · Food Science & Nutrition · doi:10.1002/fsn3.70832 · Cited by 1
In Silico Study of Natural Polyphenols as Potential Metabolic Modulators in Mitigating Lipotoxicity in Non-Alcoholic Fatty Liver Disease via Thyroid Hormone Receptor Alpha Activation
Konstantinou, E. K., Panagiotopoulos, A. A., & Dimitriou, M. (2025) · Current Issues in Molecular Biology · doi:10.3390/cimb47090777 · Cited by 1
“To ensure the reliability of our docking protocol, and based on modern guides for reliable docking methods [26], a validation study was performed in which predicted with the GalaxyWEB algorithm ligand binding poses in docking were compared with experimental THRA–ligand complex structures determined…”
Pyrrolidine‐2‐carbonitrile Derivatives as Multifunctional Antidiabetic Agents: Inhibition of α ‐Amylase, α ‐Glucosidase, and DPP‐IV With Structure‐Activity Relationships, HSA Binding, and Molecular Docking Studies
Bhat, A. A., & Singh, I. (2025) · Drug Development Research · doi:10.1002/ddr.70170 · Cited by 1
“Gaussian 09 W was used to optimize compound structures, which were then saved in PDB format and processed further in ADT by assigning charges, adding hydrogens, and converting to PDBQT format (Rizvi et al. 2013; Bender et al. 2021; Kerru et al. 2018).”
Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses
Karelina, M., & Dror, R. O. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01925 · Cited by 1
Protocol for an automated virtual screening pipeline including library generation and docking evaluation
Pereira, P. J. B., Ripoll‐Rozada, J., Macedo‐Ribeiro, S., & Manso, J. A. (2025) · STAR Protocols · doi:10.1016/j.xpro.2025.104161 · Cited by 1
Paraplume: A fast and accurate antibody paratope prediction method provides insights into repertoire-scale binding dynamics
Athènes, G., Woolfe, A., Mora, T., & Walczak, A. M. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013981 · Cited by 1
“While promising, these tools still face limitations in accuracy [4], especially at a scale required for high-throughput applications [5], and they require the 3D structures of both antibodies and antigens.”
ProMol_Func: A Structure-Free Deep Learning Model for Virtual Screening
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Combined computational and classical medicinal chemistry procedure to disclose novel pyrrole-based compounds as potential antituberculosis agents
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Cyclooxygenase-2 (COX-2) inhibitory potential of prenylated quinoline carboxylic acid derivative (PQA-18): in silico, DFT and in vitro analysis
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FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction
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“These technological advancements will enhance the diversity and comprehensiveness of our training data by providing a wider variety of protein — ligand complex conformations at an accelerated pace 58,67 .”
Integrated computational and structural understanding of Prunus bokharensis-derived β-Glucan: HRMS, network pharmacology, docking, and MD simulations to discover multi-target interactions for immunomodulation, antimelanoma lipid–hormone axis modulation, and erythroprotection
Chaudhari, S., Gaikwad, S. V., & Polshettiwar, S. A. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111710 · Cited by 1
ML-guided prioritization in virtual screening: Docking-supervised, potency-supervised, and hybrid strategies
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Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition
Moesgaard, L., Shoichet, B. K., & Mailhot, O. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01066 · Cited by 1
Self-citation
Structure based discovery of antipsychotic-like TAAR1 agonists
Huang, S., Liu, H., Wu, Y., Braz, J. M., Kranthi, D., Hall, B. W., Zhang, X., Radchenko, D. S., Moroz, Y. S., Irwin, J. J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-77484-2 · Cited by 1
“We tested our sampling and the energy potential maps usedfor scoring by askingwhetherwecouldhighly-rank known TAAR1 agonists against larger sets of property-matched 40 and extrema decoys 45 – 47 .”
Self-citation

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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