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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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“After that, the top-ranked compounds were filtered and visually inspected for prioritization, following the approach and criteria described in the literature [7, 61] (see Experimental Section for details).”
MethodSurely you are joking, Mr Docking!
Gentile, F., Oprea, T. I., Tropsha, A., & Cherkasov, A. R. (2023) · Chemical Society Reviews · doi:10.1039/d2cs00948j · Cited by 45
Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening
Kuan, J., Radaeva, M., Avenido, A., Cherkasov, A. R., & Gentile, F. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1678 · Cited by 44
“50 In contrast, large-scale docking with under-characterized targets often led to relatively modest results 37,40,41,46 or demonstrated inferior performance compared with small-scale docking.”
BackgroundResultStructure-based discovery of CFTR potentiators and inhibitors
Liu, F., Kaplan, A. L., Levring, J., Einsiedel, J., Tiedt, S., Distler, K., Omattage, N. S., Kondratov, I. S., Moroz, Y. S., Pietz, H. L., et al. (2024) · Cell · doi:10.1016/j.cell.2024.04.046 · Cited by 45
Self-citationSystematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening
Min, X., Shen, C., Yang, J., Wang, Q., & Huang, N. (2022) · ACS Omega · doi:10.1021/acsomega.2c05826 · Cited by 40
Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning
Dong, T., Yang, Z., Zhou, J., & Chen, C. Y. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00273 · Cited by 36
Gryniukova, A., Kaiser, F., Myziuk, I., Alieksieieva, D. O., Leberecht, C., Heym, P. P., Tarkhanova, O. O., Moroz, Y. S., Borysko, P., & Haupt, V. J. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00128 · Cited by 36
Yesankar, P. J., Patil, A., Kapley, A., & Qureshi, A. (2023) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-023-03617-0 · Cited by 33
Suades, A., Qureshi, A. A., McComas, S. E., Coincon, M., Rudling, A., Chatzikyriakidou, Y., Landreh, M., Carlsson, J., & Drew, D. (2023) · Nature Communications · doi:10.1038/s41467-023-39711-y · Cited by 33
“The side chain dipole moments of key residues (166, 287, 288, 324, 418) were increased to favor protein-ligand hydrogen bonds 88 .”
Self-citationSystemic evolutionary chemical space exploration for drug discovery
Lu, C., Liu, S., Shi, W., Yu, J., Zhou, Z., Zhang, X., Lu, X., Cai, F., Xia, N., & Wang, Y. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00598-4 · Cited by 30
Large-Scale Docking in the Cloud
Tingle, B. I., & Irwin, J. J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00031 · Cited by 33
“We used the docking protocol previously outlined, 17 and our benchmark used the successful docking parameters developed in an earlier study for the DRD4 receptor.”
Self-citationKallert, E., Fischer, T. R., Schneider, S., Grimm, M., Helm, M., & Kersten, C. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00751 · Cited by 31
Park, M., Baek, S., Park, S., Yi, J.-M., & Cha, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad344 · Cited by 31
“In particular, because natural products contain many compounds with similar structures that interact with multiple targets, large-scale molecular docking analysis is important for studying the mechanisms of action of natural products [12, 17, 19].”
BackgroundGrasso, D., Galderisi, S., Santucci, A., & Bernini, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065819 · Cited by 31
“…the list of compounds to be evaluated by experimental assay is slimmed down through simulated molecular docking of large compound databases [149] A critical analysis of more than 400 studies involving virtual screening of small molecule libraries showed an overall hit rate ranging from 1%…”
BackgroundPopov, K. I., Wellnitz, J., Maxfield, T., & Tropsha, A. (2023) · Molecular Informatics · doi:10.1002/minf.202300207 · Cited by 31
An explainable deep learning platform for molecular discovery
Wong, F. J., Omori, S., Li, A., Krishnan, A., Lach, R. S., Rufo, J., Wilson, M. Z., & Collins, J. J. (2024) · Nature Protocols · doi:10.1038/s41596-024-01084-x · Cited by 28
“In general, Chemprop and other chemotype-based approaches are also target independent and different from target-specific approaches, such as molecular docking 56,57 .”
Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach
Roggia, M., Natale, B., Amendola, G., Maro, S. D., & Cosconati, S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00647 · Cited by 27
MolModa: accessible and secure molecular docking in a web browser
Kochnev, Y., Ahmed, M., Maldonado, A. M., & Durrant, J. D. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae406 · Cited by 28
Kumar, V., Parate, S., Danishuddin, Zeb, A., Singh, P., Lee, G., Jung, T. S., Lee, K. W., & Ha, M. W. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.909111 · Cited by 28
“As a result, choosing an appropriate structure for molecular docking is a difficult choice; however, based on previous docking studies, researchers suggested a few key points to make a good selection, such as resolution of structure <2Å, binding with a well-known inhibitor, key molecular interaction, fewer missing res…”
BackgroundModeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00752 · Cited by 28
EasyDock: customizable and scalable docking tool
Minibaeva, G., Ivanová, A., & Полищук, П. Г. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00772-2 · Cited by 34
“7 [13], ChemFlow [14].”
Hypothesis-Driven, Structure-Based Design in Photopharmacology: The Case of eDHFR Inhibitors
Kobauri, P., Galenkamp, N. S., Schulte, A. M., de Vries, J., Simeth, N. A., Maglia, G., Thallmair, S., Kolarski, D., Szymański, W., & Feringa, B. L. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01962 · Cited by 29
Gridnev, A., Maity, S., & Misra, J. R. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1021823 · Cited by 30
“Recent developments in structure-based virtual ligand screening (VLS) allows one to conduct in silico screening of large libraries of small molecules to predict the ones, which have high probability of binding to a given site in a target protein (36-38).”
BackgroundAdvances in the discovery of new chemotypes through ultra-large library docking
Potlitz, F., Link, A., & Schulig, L. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2171984 · Cited by 30
“In addition to the issues given by the available target structure discussed here, they detail the validation of docking calculations, pre-filtering of compounds, selection of the appropriate hits, and experimental confirmation [72].”
BackgroundIn silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
Gutkin, E. M., Gusev, F., Gentile, F., Ban, F., Koby, S. B., Narangoda, C., Isayev, O., Cherkasov, A. R., & Kurnikova, M. G. (2024) · Chemical Science · doi:10.1039/d3sc06880c · Cited by 28
“11 At the same time, the current capabilities of docking so ware and the amount of available computational resources rarely allow for SBVS campaigns to surpass hundreds of millions of molecules threshold, 15 with several exceptions.”
BackgroundMeller, A., de Oliveira, S. H. P., Davtyan, A., Abramyan, T. M., Bowman, G. R., & van den Bedem, H. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1171143 · Cited by 28
“Virtual screening is a common tool for identifying novel inhibitors of proteins with known structures (Wallach et al., 2015; Lyu et al., 2019; Bender et al., 2021).”
Yu, L., He, X. D., Fang, X., Liu, L., & Liu, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01371 · Cited by 26
Ten quick tips to perform meaningful and reproducible molecular docking calculations
Martis, E. A. F., & Télétchéa, S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013030 · Cited by 31
“Generally, protocols aim for 10–20 poses per ligand (in our group the standard practice is to generate 10 poses minimum) [54] to explore binding conformations effectively.”
Anti-obesity effects of olivetol in adult zebrafish model induced by short-term high-fat diet
Reshma, A., Tamilanban, T., Chitra, V., Subramaniyan, V., Gupta, G., Fuloria, N. K., Sekar, M., Fuloria, S., Sahu, R., Narayanan, J., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-44462-3 · Cited by 26
“The results were then analyzed, and the interactions were observed using the Molegro molecular viewer 45 .”
MethodRegression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking
Marin, E., Kovaleva, M. I., Kadukova, M., Mustafin, K., Khorn, P. A., Rogachev, A. V., Mishin, A., Guskov, A., & Borshchevskiy, V. I. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01661 · Cited by 25
SARS-CoV2 billion-compound docking
Rogers, D. O. W., Agarwal, R., Vermaas, J. V., Smith, M. D., Tatikonda, R. R., Cooper, C. J., Sedova, A., Boehm, S., Baker, M. B., Gläser, J., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-01984-9 · Cited by 25
“The 1148 unique compounds tested there were divided into three sets: nonbinders (1131), hits (13), and nonspecific hits (4).”
BackgroundWellnitz, J., Jain, S., Hochuli, J. E., Maxfield, T., Muratov, E. N., Tropsha, A., & Zakharov, A. V. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00948-y · Cited by 33
“…[12], along with the exponential growth of make-on-demand chemical libraries [13] such as eMol-ecules Explore [14] and Enamines REAL Space [15], has significantly increased the appeal of using QSAR models as an alternative to structure based methods [16] in high throughput virtual screening (HTVS).”
Lam, J. H., & Katritch, V. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00031-4 · Cited by 30
“As a result, signi fi cant effort is typically devoted to assessing the quality of modeled pockets at the outset of any drug discovery campaign 58 .”
Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks
Pandey, M. K., Radaeva, M., Mslati, H., Garland, O., Fernández, M., Ester, M., & Cherkasov, A. R. (2022) · Molecules · doi:10.3390/molecules27165114 · Cited by 28
“Docking has achieved impressive advances in refining the drug-discovery pipeline and helped identify many potent and selective drug candidates [8,9].”
BackgroundRational Design in Photopharmacology with Molecular Photoswitches
Kobauri, P., Dekker, F. J., Szymański, W., & Feringa, B. L. (2023) · Angewandte Chemie · doi:10.1002/ange.202300681 · Cited by 23
Ensemble Docking for Intrinsically Disordered Proteins
Dhar, A., Sisk, T. R., & Robustelli, P. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00370 · Cited by 23
Azab, E. F. E., Saleh, A. M., Yousif, S. O., Mazhari, B. B. Z., Alrub, H. A., Elfaki, E. M., Hamza, A., & Abdulmalek, S. A. (2022) · Inflammopharmacology · doi:10.1007/s10787-022-01039-2 · Cited by 22
“The molecular docking approach may be utilized to signify the atomic level interaction between molecules and protein, allowing us to define molecule behavior in target protein binding sites as well as to elucidate the key biochemical processes (McConkey et al. 2002; Bender et al. 2021).”
BackgroundMay, L. T., Bartolo, B. A. D., Harrison, D. G., Guzik, T. J., Drummond, G. R., Figtree, G. A., Ritchie, R. H., Rye, K., & de Haan, J. B. (2022) · Clinical Science · doi:10.1042/cs20210862 · Cited by 23
A flexible data-free framework for structure-based de novo drug design with reinforcement learning
Du, H., Jiang, D., Zhang, O., Wu, Z., Gao, J., Zhang, X., Wang, X., Deng, Y., Kang, Y., Li, D., et al. (2023) · Chemical Science · doi:10.1039/d3sc04091g · Cited by 24
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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