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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There are advances in computationally modeling how small molecules and proteins interact via docking.
“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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BackgroundMachine learning classification can reduce false positives in structure-based virtual screening
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BackgroundMethodInfluentialSelf-citationGenerating Multibillion Chemical Space of Readily Accessible Screening Compounds
Grygorenko, O. O., Radchenko, D. S., Dziuba, I., Chuprina, A., Gubina, K. E., & Moroz, Y. S. (2020) · iScience · doi:10.1016/j.isci.2020.101681 · Cited by 223 · cited 3× · see citing works
“At Enamine, a pioneer in this area, >140 reactions may be used to synthesize products from among >120,000 distinct and often highly stereogenic building blocks, leading to a remarkably diverse and, critically, pragmatically accessible library of currently over 29 billion molecules 11 .”
BackgroundStoichiometry and Physical Chemistry of Promiscuous Aggregate-Based Inhibitors
Coan, K. E. D., & Shoichet, B. K. (2008) · Journal of the American Chemical Society · doi:10.1021/ja802977h · Cited by 222 · see citing works
Self-citationStructures of the σ2 receptor enable docking for bioactive ligand discovery
Alon, A., Lyu, J., Braz, J. M., Tummino, T. A., Craik, V., O’Meara, M. J., Webb, C. M., Radchenko, D. S., Moroz, Y. S., Huang, X.-P., et al. (2021) · Nature · doi:10.1038/s41586-021-04175-x · Cited by 211 · see citing works
Self-citationProtocols for Molecular Modeling with Rosetta3 and RosettaScripts
Bender, B. J., Cisneros, A., Duran, A. M., Finn, J. A., Fu, D., Lokits, A. D., Mueller, B. K., Sangha, A. K., Sauer, M. F., Sevy, A. M., et al. (2016) · Biochemistry · doi:10.1021/acs.biochem.6b00444 · Cited by 206 · see citing works
Self-citationde Graaf, C., Kooistra, A. J., Vischer, H. F., Katritch, V., Kuijer, M., Shiroishi, M., Iwata, S., Shimamura, T., Stevens, R. C., de Esch, I. J. P., et al. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm2011589 · Cited by 205 · see citing works
Promiscuous Aggregate-Based Inhibitors Promote Enzyme Unfolding
Coan, K. E. D., Maltby, D. A., Burlingame, A. L., & Shoichet, B. K. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm801605r · Cited by 203 · see citing works
Self-citationSchuller, M., Correy, G. J., Gahbauer, S., Fearon, D., Wu, T., Díaz, R. E., Young, I. D., Martins, L. C., Smith, D. H., Schulze‐Gahmen, U., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf8711 · Cited by 195 · see citing works
Self-citationKinase hinge binding scaffolds and their hydrogen bond patterns
Li, X., Klug-McLeod, J. L., Rai, B., & Lunney, E. A. (2015) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2015.08.006 · Cited by 190 · cited 1× · see citing works
“For example, hydrogen bonding to kinase hinge residues 131 , or salt bridges to the conserved aspartic acid D3.”
BackgroundArmstrong, J. F., Faccenda, E., Harding, S. D., Pawson, A. J., Southan, C., Sharman, J. L., Campo, B., Cavanagh, D. R., Alexander, S. P. H., Davenport, A. P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz951 · Cited by 186 · see citing works
Bottegoni, G., Kufareva, I., Totrov, M. M., & Abagyan, R. A. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8009958 · Cited by 181 · see citing works
Bender, A., & Glen, R. C. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0500177 · Cited by 179 · see citing works
Babaoglu, K., Simeonov, A. M., Irwin, J. J., Nelson, M. E., Feng, B. Y., Thomas, C. J., Cancian, L., Costi, M. P., Maltby, D. A., Jadhav, A., et al. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm701500e · Cited by 178 · see citing works
Self-citationRecipes for the Selection of Experimental Protein Conformations for Virtual Screening
Rueda, M., Bottegoni, G., & Abagyan, R. A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9003943 · Cited by 176 · see citing works
Alexander, N. S., Woetzel, N., & Meiler, J. (2011) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2011.5729867 · Cited by 172 · see citing works
Small-Molecule Activators of a Proenzyme
Wolan, D. W., Zorn, J. A., Gray, D. C., & Wells, J. A. (2009) · Science · doi:10.1126/science.1177585 · Cited by 171 · cited 1× · see citing works
“This aggregates sequester proteins with little selectivity, partially denaturing and inhibiting them 32,92,94 – 96 , occasionally even activating them 97,98 , a common problem both in HTS and also in docking screens 92 .”
BackgroundColloidal Aggregation Affects the Efficacy of Anticancer Drugs in Cell Culture
Owen, S. C., Doak, A. K., Wassam, P., Shoichet, M. S., & Shoichet, B. K. (2012) · ACS Chemical Biology · doi:10.1021/cb300189b · Cited by 170 · see citing works
Self-citationCavasotto, C. N., Orry, A. J. W., Murgolo, N., Czarniecki, M., Kocsi, S. A., Hawes, B. E., O’Neill, K. A., Hine, H., Burton, M. S., Voigt, J. H., et al. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm070759m · Cited by 167 · see citing works
FTMAP: extended protein mapping with user-selected probe molecules
Ngan, C. H., Bohnuud, T., Mottarella, S. E., Beglov, D. B., Villar, E. A., Hall, D. R., Kozakov, D., & Vajda, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks441 · Cited by 154 · cited 1× · see citing works
“If there is no available holo structure, tools such as SphGen 34 , SiteMap 35 and FTMap 36 can be used to identify potential ligand binding sites.”
MethodTikhonova, I. G., Sum, C. S., Neumann, S., Engel, S., Raaka, B. M., Costanzi, S., & Gershengorn, M. C. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm7012425 · Cited by 148 · see citing works
Katritch, V., Rueda, M., Lam, P. C., Yeager, M. J., & Abagyan, R. A. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22507 · Cited by 141 · see citing works
Property-Unmatched Decoys in Docking Benchmarks
Stein, R. M., Ying, Y., Balius, T. E., O’Meara, M. J., Lyu, J., Young, J., Tang, K. G., Shoichet, B. K., & Irwin, J. J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00598 · Cited by 136 · cited 15× · see citing works
“5) prepares a starting structure (Steps 1 – 4), generates the scoring grids and matching spheres (Steps 5 – 10), and collects a set of control ligands for model evaluation (Steps 11 – 33).”
BackgroundMethodInfluentialSelf-citationIdentification of Novel Adenosine A2A Receptor Antagonists by Virtual Screening
Langmead, C. J., Andrews, S. P., Congreve, M., Errey, J. C., Hurrell, E., Marshall, F. H., Mason, J. S., Richardson, C. M., Robertson, N. J., Zhukov, A., et al. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm201455y · Cited by 135 · see citing works
Electrostatic Contributions to Heat Capacity Changes of DNA-Ligand Binding
Gallagher, K., & Sharp, K. A. (1998) · Biophysical Journal · doi:10.1016/s0006-3495(98)77566-6 · Cited by 135 · cited 1× · see citing works
“Electrostatic potentials within the binding pocket are estimated by numerically solving the Poisson – Boltzmann equation using QNIFFT 16,110 .”
MethodThorne, N., Shen, M., Lea, W., Simeonov, A. M., Lovell, S., Auld, D. S., & Inglese, J. (2012) · Chemistry & Biology · doi:10.1016/j.chembiol.2012.07.015 · Cited by 133 · see citing works
Docking Screens: Right for the Right Reasons?
Kolb, P., & Irwin, J. J. (2009) · Current Topics in Medicinal Chemistry · doi:10.2174/156802609789207091 · Cited by 132 · see citing works
Self-citationStructure-based ligand discovery for the protein–protein interface of chemokine receptor CXCR4
Mysinger, M. M., Weiss, D. R., Ziarek, J. J., Gravel, S., Doak, A. K., Karpiak, J. D., Heveker, N., Shoichet, B. K., & Volkman, B. F. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120431109 · Cited by 129 · cited 12× · see citing works
“5) prepares a starting structure (Steps 1 – 4), generates the scoring grids and matching spheres (Steps 5 – 10), and collects a set of control ligands for model evaluation (Steps 11 – 33).”
BackgroundInfluentialSelf-citationExploiting Ordered Waters in Molecular Docking
Huang, N., & Shoichet, B. K. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8006239 · Cited by 129 · cited 2× · see citing works
“Consequently, these scoring grids can be fi ne-tuned by modulating the protein – solvent dielectric interface (see below Steps 41 – 44).”
BackgroundSelf-citationVirtual Screening against Metalloenzymes for Inhibitors and Substrates
Irwin, J. J., Raushel, F. M., & Shoichet, B. K. (2005) · Biochemistry · doi:10.1021/bi050801k · Cited by 121 · cited 1× · see citing works
“For a given target, these can be found in the scienti fi c literature, patent literature or public databases such as IUPHAR/BPS 114 , ChEMBL 115 or ZINC 9,99,116 , or available in-house.”
BackgroundSelf-citationStructure-Based Discovery of a Novel, Noncovalent Inhibitor of AmpC β-Lactamase
Powers, R. A., Morandi, F., & Shoichet, B. K. (2002) · Structure · doi:10.1016/s0969-2126(02)00799-2 · Cited by 121 · cited 1× · see citing works
“…D 4 dopamine, histamine H 1 and A 2a adenosine receptors 37 – 40 typically have higher hit rates and more potent docking hits than do peptide receptors like the CXCR4 receptor 41 , and these often perform better than the more open sites typical of soluble enzymes like β -lactamase 12,42 .”
BackgroundSelf-citationSharp, K. A., Friedman, R. A., Misra, V. K., Hecht, J. L., & Honig, B. H. (1995) · Biopolymers · doi:10.1002/bip.360360211 · Cited by 118 · cited 1× · see citing works
“Electrostatic potentials within the binding pocket are estimated by numerically solving the Poisson – Boltzmann equation using QNIFFT 16,110 .”
MethodCho, Y., Ioerger, T. R., & Sacchettini, J. C. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm800328v · Cited by 110 · see citing works
Colloidal Aggregation Causes Inhibition of G Protein-Coupled Receptors
Sassano, M. F., Doak, A. K., Roth, B. L., & Shoichet, B. K. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm301749y · Cited by 110 · cited 1× · see citing works
“Control for the effect of the detergent on a well-behaved, nonaggregating ligand, which should be unaffected 93,142,143 Test for the formation of particles >30 nm in radii by dynamic light scattering 92,144 For assays that will not tolerate detergent, an inert protein can be added to…”
BackgroundSelf-citationPredicting the accuracy of protein–ligand docking on homology models
Bordogna, A., Pandini, A., & Bonati, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21601 · Cited by 107 · see citing works
Docking for fragment inhibitors of AmpC β-lactamase
Teotico, D. G., Babaoglu, K., Rocklin, G. J., Ferreira, R. S., Giannetti, A. M., & Shoichet, B. K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0813029106 · Cited by 102 · see citing works
Self-citationAllen, W. J., & Rizzo, R. C. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400534h · Cited by 98 · cited 1× · see citing works
“The validity of predicted binding poses can be assessed qualitatively by visual inspection of reported key interactions between protein and ligand or, in the best case, quantitatively by calculating root mean square deviations between predicted and experimentally determined poses 82 .”
BackgroundThe Chemical Basis of Pharmacology
Keiser, M. J., Irwin, J. J., & Shoichet, B. K. (2010) · Biochemistry · doi:10.1021/bi101540g · Cited by 94 · cited 1× · see citing works
“While the libraries physically screened in HTS were an enormous expansion on those used by classical, pre-molecular pharmacology 3 , they nevertheless represent only a tiny fraction of possible ‘ drug-like ’ molecules 4 .”
MethodSelf-citationDocking ligands onto binding site representations derived from proteins built by homology modelling
Schafferhans, A., & Klebe, G. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.4453 · Cited by 90 · cited 1× · see citing works
“…56,57 will help prevent the pocket from collapsing inward, and will better orient the side chains of binding residues 32,58,59 When it is unknown how a ligand binds within the pocket, orthogonal experimental data can guide the modeling such as iterating between docking and modeling 60,61 .”
BackgroundGreenidge, P. A., Krämer, C., Mozziconacci, J., & Sherman, W. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5003735 · Cited by 87 · see citing works
Discovery of Small Molecule CD40–TRAF6 Inhibitors
Zarzycka, B. A., Seijkens, T. T. P., Nabuurs, S. B., Ritschel, T., Grommes, J., Soehnlein, O., Schrijver, R., van Tiel, C. M., Hackeng, T. M., Weber, C., et al. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500631e · Cited by 82 · cited 1× · see citing works
“In most cases, targeting protein – protein interaction surfaces, outside of a few that are well de fi ned 43 , often leads to disappointing results.”
BackgroundTesting inhomogeneous solvation theory in structure-based ligand discovery
Balius, T. E., Fischer, M. M., Stein, R. M., Adler, T. B., Nguyen, C. N., Cruz, A., Gilson, M. K., Kurtzman, T., & Shoichet, B. K. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1703287114 · Cited by 80 · see citing works
Self-citationAn allosteric modulator binds to a conformational hub in the β2 adrenergic receptor
Liu, X., Kaindl, J. M., Korczynska, M., Stößel, A., Dengler, D. G., Stanek, M., Hübner, H., Clark, M. J., Mahoney, J., Matt, R. A., et al. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0549-2 · Cited by 79 · see citing works
Self-citationMahmoud, A. H., Masters, M. R., Ying, Y., & Lill, M. A. (2020) · Communications Chemistry · doi:10.1038/s42004-020-0261-x · Cited by 76 · see citing works
Virtual Screening in the Cloud: How Big Is Big Enough?
Grebner, C., Malmerberg, E., Shewmaker, A., Batista, J., Nicholls, A. O., & Sadowski, J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00779 · Cited by 72 · see citing works
Patel, H., Ihlenfeldt, W. D., Judson, P. N., Moroz, Y. S., Pevzner, Y., Peach, M. L., Delannée, V., Tarasova, N. I., & Nicklaus, M. C. (2020) · Scientific Data · doi:10.1038/s41597-020-00727-4 · Cited by 67 · see citing works
Weichenberger, C. X., & Sippl, M. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm263 · Cited by 64 · see citing works
Eberini, I., Daniele, S., Parravicini, C., Sensi, C., Trincavelli, M. L., Martini, C., & Abbracchio, M. P. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9455-8 · Cited by 63 · see citing works
Receptor structure-based discovery of non-metabolite agonists for the succinate receptor GPR91
Trauelsen, M., Ulven, E. R., Hjorth, S. A., Brvar, M., Monaco, C. G., Frimurer, T. M., & Schwartz, T. W. (2017) · Molecular Metabolism · doi:10.1016/j.molmet.2017.09.005 · Cited by 63 · see citing works
Ligand-Steered Modeling and Docking: A Benchmarking Study in Class A G-Protein-Coupled Receptors
Phatak, S. S., Gatica, E. A., & Cavasotto, C. N. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100285f · Cited by 60 · cited 1× · see citing works
“…the highest identity in the binding pocket ● Incorporation of a ligand during the modeling process or ligand-steered homology modeling approaches 56,57 will help prevent the pocket from collapsing inward, and will better orient the side chains of binding residues 32,58,59 When it is unknown how a…”
BackgroundLigand Strain Energy in Large Library Docking
Gu, S., Smith, M. S., Ying, Y., Irwin, J. J., & Shoichet, B. K. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00368 · Cited by 60 · see citing works
Self-citationKolb, P., Phan, K., Gao, Z., Marko, A. C., Šali, A., & Jacobson, K. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049910 · Cited by 53 · cited 10× · see citing works
“5) prepares a starting structure (Steps 1 – 4), generates the scoring grids and matching spheres (Steps 5 – 10), and collects a set of control ligands for model evaluation (Steps 11 – 33).”
MethodInfluentialTemplate‐based protein structure prediction in CASP11 and retrospect of I‐TASSER in the last decade
Yang, J., Zhang, W., He, B., Walker, S. E., Zhang, H., Govindarajoo, B., Virtanen, J., Xue, Z., Shen, H., & Zhang, Y. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24918 · Cited by 50 · see citing works
Stable Colloidal Drug Aggregates Catch and Release Active Enzymes
McLaughlin, C. K., Duan, D., Ganesh, A. N., Torosyan, H., Shoichet, B. K., & Shoichet, M. S. (2016) · ACS Chemical Biology · doi:10.1021/acschembio.5b00806 · Cited by 49 · cited 1× · see citing works
“…nonaggregating ligand, which should be unaffected 93,142,143 Test for the formation of particles >30 nm in radii by dynamic light scattering 92,144 For assays that will not tolerate detergent, an inert protein can be added to prophylactically coat the colloids, protecting the assayed target…”
BackgroundSelf-citationFibrils Colocalize Caspase-3 with Procaspase-3 to Foster Maturation
Zorn, J. A., Wolan, D. W., Agard, N. J., & Wells, J. A. (2012) · Journal of Biological Chemistry · doi:10.1074/jbc.m112.386128 · Cited by 47 · see citing works
Using RosettaLigand for Small Molecule Docking into Comparative Models
Kaufmann, K. W., & Meiler, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050769 · Cited by 46 · see citing works
Brajesh, K., Sresht, V., Yang, Q., Unwalla, R. J., Tu, M., Mathiowetz, A. M., & Bakken, G. A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00373 · Cited by 41 · see citing works
Jaiteh, M., Rodríguez‐Espigares, I., Selent, J., & Carlsson, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007680 · Cited by 41 · cited 2× · see citing works
“These two principles can improve the chances of success: ● Typically, the higher the sequence identity between the target and the template, the better the accuracy of the model 55 .”
BackgroundSelf-citationColloidal Drug Aggregate Stability in High Serum Conditions and Pharmacokinetic Consequence
Ganesh, A. N., Aman, A. M., Logie, J., Barthel, B. L., Cogan, P. S., Al‐awar, R., Koch, T. H., Shoichet, B. K., & Shoichet, M. S. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00032 · Cited by 38 · see citing works
Self-citationMännel, B., Jaiteh, M., Zeifman, A. A., Randáková, A., Weikert, D., Hübner, H., Gmeiner, P., & Carlsson, J. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.7b00493 · Cited by 37 · see citing works
Self-citationFrimurer, T. M., Mende, F., Graae, A.-S., Engelstoft, M. S., Egerod, K. L., Nygaard, R., Gerlach, L., Hansen, J. B., Schwartz, T. W., & Holst, B. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00648 · Cited by 35 · see citing works
Martí-Solano, M., Schmidt, D., Kolb, P., & Selent, J. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.01.009 · Cited by 32 · see citing works
Daga, P., Polgar, W. E., & Zaveri, N. T. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500291a · Cited by 28 · see citing works
Wu, Y., Lou, L., & Xie, Z. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00081 · Cited by 24 · cited 1× · see citing works
“Previous studies suggest that hits from virtual screening generally compare fairly well with experimental high-resolution structures; i.e., key anchor points are predicted correctly 12,83,102,103 .”
BackgroundLi, S., Zhou, Y., Lü, W., Zhong, Y., Song, W., Liu, K., Huang, J. Y., Zhao, Z., Xu, Y. F., Liu, X., et al. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2002445 · Cited by 21 · see citing works
Pattern recognition and massively distributed computing
Davies, E. K., Glick, M., Harrison, K. N., & Richards, W. G. (2002) · Journal of Computational Chemistry · doi:10.1002/jcc.10107 · Cited by 20 · cited 1× · see citing works
“…two limitations that have prevented wide-scale adoption: the virtual libraries have rarely been carefully curated for true synthetic accessibility 8 , and there were well-founded concerns that computational methods, such as molecular docking, were not accurate enough to prioritize true hits…”
BackgroundHomology modeling and substrate binding study of human CYP2C18 and CYP2C19 enzymes
Payne, V. A., Chang, Y., & Loew, G. H. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19991101)37:2 · Cited by 20 · see citing works
Identification of diverse dipeptidyl peptidase IV inhibitors via structure-based virtual screening
Cui, L., Lü, W., Lü, C., Xiao, W. J., Shen, X., Huang, J. Y., Liu, G., & Tang, Y. (2012) · Journal of Molecular Modeling · doi:10.1007/s00894-012-1394-3 · Cited by 19 · see citing works
Khare, P. S., Gupta, A. K., Gajula, P. K., Sunkari, K. Y., Jaiswal, A. K., Das, S., Bajpai, P., Chakraborty, T. K., Dube, A., & Saxena, A. K. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci2005862 · Cited by 15 · see citing works
Diaz, C., Bouteiller, C. L., Yvon, S., Cambon‐Kernëis, A., Roasio, A., Jamme, M. F., Aries, A., Feuillerat, C., Perret, E., Guette, F., et al. (2013) · Molecular Informatics · doi:10.1002/minf.201200047 · Cited by 15 · see citing works
Bandyopadhyay, D., Bhatnagar, A., Jain, S., & Pratyaksh, P. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c02629 · Cited by 15 · see citing works
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- [113]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). 23579614
- [118]Alon A Crystal structures of the σ 2 receptor template large-library docking for selective chemotypes active in vivo. Preprint at bioRxiv 10.1101/2021.04.29.441652 (2021). · DOI
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Sorted by citations
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,342 · see citing works
“%) hit rates in experimental testing 60 , yielding novel hits for many targets with potencies in the 0.1–10-μM range (for those that are published, at least).”
BackgroundInfluentialThe Art and Science of Molecular Docking
Paggi, J. M., Pandit, A., & Dror, R. O. (2024) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-030222-120000 · Cited by 511 · see citing works
Gentile, F., Yaacoub, J. C., Gleave, J., Fernandez, M., Ton, A.-T., Ban, F., Stern, A., & Cherkasov, A. (2022) · Nature Protocols · doi:10.1038/s41596-021-00659-2 · Cited by 397 · see citing works
“Very recently, Bender et al. developed a guide for ultra-large docking campaigns, highlighting that computing clusters of 500 – 1,000 CPU cores are required to perform billion-scale virtual screening in a timely manner 46 .”
ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries
Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae416 · Cited by 392 · see citing works
“Both HTMD and generative AI approaches yield a large number of potential hits with high efficacy, but many of these hits are not ideal for therapeutic development because they do not possess druglike properties (Bender et al. 2021).”
BackgroundBenchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211081 · Cited by 284 · see citing works
“In view of these observations, it is important to note that docking has been widely used in “one target, many compounds” approaches to enrich for screening hits (Lyu et al , 2019; Bender et al , 2021).”
BackgroundMethodInfluentialRational Design in Photopharmacology with Molecular Photoswitches
Kobauri, P., Dekker, F. J., Szymański, W., & Feringa, B. L. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202300681 · Cited by 265 · see citing works
Luttens, A., Gullberg, H., Abdurakhmanov, E., Vo, D. D., Akaberi, D., Talibov, V., Nekhotiaeva, N., Vangeel, L., Jonghe, S. D., Jochmans, D., et al. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.1c08402 · Cited by 245 · see citing works
Self-citationXue, Q., Xian, L., Russell, P. J., Li, J., Pan, W., Fu, J., & Zhang, A. (2022) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2022.113323 · Cited by 233 · see citing works
(2024) · doi:10.1038/s41467-024-45461-2 · Cited by 243 · see citing works
“Conventional docking protocols usually perform protein conformation sampling as a separate step from the docking process 15,38 .”
Machine learning in preclinical drug discovery
Catacutan, D. B., Alexander, J., Arnold, A., & Stokes, J. M. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01679-1 · Cited by 219 · see citing works
“%) of molecules from the virtual library and docks them using conventional methods 26–30 .”
InfluentialModeling the expansion of virtual screening libraries
Lyu, J., Irwin, J. J., & Shoichet, B. K. (2023) · Nature Chemical Biology · doi:10.1038/s41589-022-01234-w · Cited by 193 · see citing works
“First, they are rare events; if they were more common, they would be discovered by the retrospective control screens that are commonly conducted before a large prospective screen 40 .”
BackgroundSelf-citationAdvances in artificial intelligence for drug delivery and development: A comprehensive review
Gholap, A. D., Uddin, M. J., Faiyazuddin, M., Omri, A., Gowri, S., & Khalid, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108702 · Cited by 195 · see citing works
G Protein-Coupled Receptors: A Century of Research and Discovery
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“219,220 Surprisingly, of the synthesized compounds, 15 compounds showed activity, with a final hit rate of close to 40% using this screening strategy.”
Phytochemicals in Drug Discovery—A Confluence of Tradition and Innovation
Chihomvu, P., Ganesan, A., Gibbons, S., Johannson, M., Woollard, K., & Hayes, M. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25168792 · Cited by 155 · see citing works
“Fortunately, computational tools like molecular modelling or docking enable researchers to expect interactions between generated molecules and their intended targets while also determining the chemical properties of these agents so they can assign priority levels before testing [111,112].”
BackgroundStructure-based discovery of nonopioid analgesics acting through the α 2A -adrenergic receptor
Fink, E. A., Xu, J. Z., Hübner, H., Bráz, J. M., Seemann, P., Avet, C., Craik, V., Weikert, D., Schmidt, M. F., Webb, C. M., et al. (2022) · Science · doi:10.1126/science.abn7065 · Cited by 152 · see citing works
“In control calculations (61) with 15 known agonists from the IUPHAR-BPS database (27) and from the literature (29–31), balanced against 1800 property matched decoys (62), docking parameters were optimized based on adjusted logAUC (61) and based on recapitulation of ligand interactions with residues a2BAR D92 , F412, F…”
MethodSelf-citationMolecular Glue Discovery: Current and Future Approaches
Dewey, J. A., Delalande, C., Azizi, S., Lu, V., Antonopoulos, D. A., & Babnigg, G. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00449 · Cited by 147 · see citing works
Role of artificial intelligence in revolutionizing drug discovery
Rehman, A. U., Li, M., Wu, B., Ali, Y., Rasheed, S., Shaheen, S., Liu, X., Luo, R., & Zhang, J. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.04.021 · Cited by 139 · see citing works
“Bender et al.’s guidelines for large-scale docking benchmarking are helpful [ 122 ].”
DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design
García-Ortegón, M., Simm, G. N. C., Tripp, A., Hernández-Lobato, J. M., Bender, A., & Bacallado, S. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01334 · Cited by 137 · see citing works
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“In a second docking campaign, scoring parameters were optimized based on the results from the computational and crystallographic fragment screens as well as the first lead-like docking campaign (23).”
Self-citationMolecular Filters in Medicinal Chemistry
Kralj, S., Jukič, M., & Bren, U. (2023) · Encyclopedia · doi:10.3390/encyclopedia3020035 · Cited by 101 · see citing works
“The same group wonderfully summarized the effective use of filtering in a practical guide to virtual screening [56].”
BackgroundComprehensive Survey of Consensus Docking for High-Throughput Virtual Screening
Blanes‐Mira, C., Fernández-Aguado, P., de Andrés-López, J., Fernández-Carvajal, A. M., Ferrer-Montiel, A. V., & Fernandez-Ballester, G. J. (2022) · Molecules · doi:10.3390/molecules28010175 · Cited by 106 · see citing works
“Other factors should be considered for target selection and optimization to achieve a successful docking [66].”
BackgroundA practical guide to machine-learning scoring for structure-based virtual screening
Tran‐Nguyen, V., Junaid, M., Simeon, S., & Ballester, P. J. (2023) · Nature Protocols · doi:10.1038/s41596-023-00885-w · Cited by 97 · see citing works
Hasan, M. R., Alsaiari, A. A., Fakhurji, B., Molla, M. H. R., Asseri, A. H., Sumon, M. A. A., Park, M. N., Ahammad, F., & Kim, B. (2022) · Molecules · doi:10.3390/molecules27134169 · Cited by 99 · see citing works
“CADD techniques include a molecular docking methodology that can effectively categorize a large number of molecules with higher binding effectiveness [18].”
The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy
Ivanova, L. B., & Karelson, M. M. (2022) · Molecules · doi:10.3390/molecules27249041 · Cited by 100 · see citing works
“The predicted binding free energy is one of the main parameters that allow for the initial separation of possible binding ligands from molecules that are unlikely to bind a target [9,10].”
Structure-based discovery of conformationally selective inhibitors of the serotonin transporter
Singh, I., Seth, A., Billesbølle, C. B., Bráz, J. M., Rodriguiz, R. M., Roy, K., Bekele, B., Craik, V., Huang, X., Boytsov, D., et al. (2023) · Cell · doi:10.1016/j.cell.2023.04.010 · Cited by 99 · see citing works
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“As Bender et al. stated in their practical guide to large-scale docking 45 , ‘any structure-based campaign begins with a suitable target site’.”
DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening
Guedes, I. A., da Silva, M. M. P., Galheigo, M. M., Krempser, E., de Magalhães, C. S., Barbosa, H. J. C., & Dardenne, L. E. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168548 · Cited by 89 · see citing works
Structure and dynamics in drug discovery
Wei, H., & McCammon, J. A. (2024) · npj Drug Discovery. · doi:10.1038/s44386-024-00001-2 · Cited by 94 · see citing works
“Nowadays,screeningson ultra-largevirtuallibrariesthatincludebillionsofdrug-likecompoundsare feasible,thankstotherecentavailabilityofcloudcomputingandgraphics processing unit (GPU) computing resources 36 .”
InfluentialStructure-based virtual screening of vast chemical space as a starting point for drug discovery
Carlsson, J., & Luttens, A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102829 · Cited by 78 · see citing works
“[19]. approximations are made in docking algorithms.”
BackgroundSelf-citationRapid traversal of vast chemical space using machine learning-guided docking screens
Luttens, A., de Vaca, I. C., Sparring, L., Brea, J. M., Martínez, A. L., Kahlous, N. A., Radchenko, D. S., Moroz, Y. S., Loza, M. I., Norinder, U., et al. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00777-x · Cited by 79 · see citing works
“7 to improve docking performance and has been used in previous virtual screens 50 .”
Self-citationAssessing molecular docking tools: understanding drug discovery and design
Nivatya, H. K., Singh, A., Kumar, N., Sonam, Sharma, L., Singh, V., Mishra, R., Gaur, N., & Mishra, A. K. (2025) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-025-00862-y · Cited by 92 · see citing works
“The identification of the receptor–ligand complex’s lowest free energy configurations [224].”
Discovering small-molecule senolytics with deep neural networks
Wong, F. J., Omori, S., Donghia, N. M., Zheng, E. J., & Collins, J. J. (2023) · Nature Aging · doi:10.1038/s43587-023-00415-z · Cited by 77 · see citing works
Dockey: a modern integrated tool for large-scale molecular docking and virtual screening
Du, L., Geng, C., Zeng, Q., Huang, T. L., Tang, J., Chu, Y., & Zhao, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad047 · Cited by 75 · see citing works
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“…we took into account that several energy terms are either poorly described by the docking scoring function (e.g., desolvation penalties for unsatisfied hydrogen bond donors and acceptors) or not considered at all (e.g., ligand strain and receptor desolvation), as described previously ( 37 ).”
InfluentialSelf-citationVitale, G. A., Geibel, C., Minda, V., Wang, M., Aron, A. T., & Petras, D. (2024) · Natural Product Reports · doi:10.1039/d3np00050h · Cited by 66 · see citing works
The impact of library size and scale of testing on virtual screening
Liu, F., Mailhot, O., Glenn, I., Vigneron, S. F., Bassim, V., Xu, X., Fonseca-Valencia, K., Smith, M. S., Radchenko, D. S., Fraser, J. S., et al. (2025) · Nature Chemical Biology · doi:10.1038/s41589-024-01797-w · Cited by 66 · see citing works
“8 scoring function 22 .”
Self-citationThompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases
Klarich, K., Goldman, B. B., Kramer, T. J., Riley, P., & Walters, W. P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01790 · Cited by 57 · see citing works
Vincenzi, M., Mercurio, F. A., & Leone, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031798 · Cited by 60 · see citing works
“SBVS of compound libraries is a widely employed approach at the initial stages of the drug discovery process to support hit-finding campaigns [93].”
BackgroundInfluentialPerspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development
Nam, K., Shao, Y., Major, D. T., & Wolf‐Watz, M. (2024) · ACS Omega · doi:10.1021/acsomega.3c09084 · Cited by 57 · see citing works
Machine-learned molecular mechanics force fields from large-scale quantum chemical data
Takaba, K., Friedman, A. J., Cavender, C. E., Behara, P. K., Pulido, I., Henry, M. M., MacDermott-Opeskin, H., Iacovella, C. R., Nagle, A., Payne, A. M., et al. (2024) · Chemical Science · doi:10.1039/d4sc00690a · Cited by 56 · see citing works
Molecular Docking in Drug Discovery: Techniques, Applications, and Advancements
Aguiar, C., & Camps, I. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673325827240926081845 · Cited by 52 · see citing works
“At its core, molecular docking simulates the “lock-and-key” mechanism that underlies molecular recognition, which is pivotal for the identification and optimization of compounds with therapeutic potential [1–4].”
Considerations and challenges for sex-aware drug repurposing
Fisher, J. L., Jones, E. F., Flanary, V. L., Williams, A. S., Wilk, E. J., & Lasseigne, B. N. (2022) · Biology of Sex Differences · doi:10.1186/s13293-022-00420-8 · Cited by 47 · see citing works
“Some molecular docking methods have reduced the complexity of calculations by approximating and removing certain parameters to increase computational speeds, but this has increased docking energy errors and unreliable ranks of drug candidates (105).”
BackgroundRuan, G., Ye, L., Lin, J., Lin, H., Yu, L., Wang, C., Mao, X., Zhang, S., & Sun, P. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04048-z · Cited by 49 · see citing works
“Then, the probable active compounds and target proteins were further screened in a molecular docking approach that defines the behavior of small molecules in the binding site of proteins and seeks to clarify essential biochemical processes [13].”
Accelerating therapeutic protein design with computational approaches toward the clinical stage
Chen, Z., Wang, X., Chen, X., Huang, J., Wang, C., Wang, J., & Wang, Z. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.04.027 · Cited by 49 · see citing works
“…discovery fall into two categories: physics-based multi-scale modeling methods [31] (e.g. Rosetta [32], molecular dynamics simulation [33,34], molecular docking [35]) and data-driven methods based on big data and data-mining (e.g. artificial intelligence, deep learning, machine learning) [36–39].”
MethodStructure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase
Singh, I., Li, F., Fink, E. A., Chau, I., Li, A. S. M., Rodríguez‐Hernández, A., Glenn, I., Belinchón, F. J. Z., Rodriguez, M. L., Devkota, K., et al. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c02120 · Cited by 49 · see citing works
Self-citationLarge library docking identifies positive allosteric modulators of the calcium-sensing receptor
Liu, F., Wu, C.-G., Tu, C., Glenn, I., Meyerowitz, J., Levit, A., Lyu, J., Cheng, Z., Tarkhanova, O. O., Moroz, Y. S., et al. (2024) · Science · doi:10.1126/science.ado1868 · Cited by 49 · see citing works
“Different dielectric and desolvation volumes weresampledto maximizeenrichment (ability to prioritize ligands over nonligands) and geometric fidelity of the known molecules and to bring the van der Waals and polar components into rough balance in retrospective control calculations ( 64 ).”
BackgroundSelf-citationAdvancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works
Gorgulla, C., Jayaraj, A., Fackeldey, K., & Arthanari, H. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2022.102156 · Cited by 49 · see citing works
Paragraph—antibody paratope prediction using graph neural networks with minimal feature vectors
Chinery, L., Wahome, N., Moal, I. H., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac732 · Cited by 47 · see citing works
Pareto optimization to accelerate multi-objective virtual screening
Fromer, J. C., Graff, D., & Coley, C. W. (2024) · Digital Discovery · doi:10.1039/d3dd00227f · Cited by 46 · see citing works
Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling
Taldaev, A., Terekhov, R. P., Nikitin, I. D., Жевлакова, А. К., & Selivanova, I. A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23116023 · Cited by 46 · see citing works
“The visual control of docking results is an essential part of in silico research that helps exclude the presence of structural artifacts that are formed by computer calculation [95].”
BackgroundRecent Developments in Ultralarge and Structure-Based Virtual Screening Approaches
Gorgulla, C. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020222-025013 · Cited by 50 · see citing works
Large library docking for novel SARS‐CoV ‐2 main protease non‐covalent and covalent inhibitors
Fink, E. A., Bardine, C., Gahbauer, S., Singh, I., Detomasi, T. C., White, K. M., Gu, S., Wan, X., Chen, J. S., Ary, B. E., et al. (2023) · Protein Science · doi:10.1002/pro.4712 · Cited by 43 · see citing works
“In turn, the partial charges of oxygen atoms of the corresponding backbone groups were decreased by 0.4e to maintain the initial net charge of each residue (Bender et al., 2021).”
InfluentialSelf-citationBegnini, F., Geschwindner, S., Johansson, P., Wissler, L., Lewis, R. J., Danelius, E., Luttens, A., Matricon, P. G., Carlsson, J., Lenders, S., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01975 · Cited by 43 · see citing works
Self-citationDiffBindFR: an SE(3) equivariant network for flexible protein–ligand docking
Zhu, J., Gu, Z., Pei, J., & Lai, L. (2024) · Chemical Science · doi:10.1039/d3sc06803j · Cited by 44 · see citing works
Singh, M. B., Jain, P., Tomar, J., Kumar, V., Bahadur, I., Arya, D. K., & Singh, P. (2022) · Journal of the Indian Chemical Society · doi:10.1016/j.jics.2022.100433 · Cited by 42 · see citing works
An Overview of the Potential Medicinal and Pharmaceutical Properties of Ru(II)/(III) Complexes
Skoczyńska, A., Lewiñski, A., Pokora, M., Paneth, P., & Budzisz, E. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24119512 · Cited by 41 · see citing works
Docking for EP4R antagonists active against inflammatory pain
Gahbauer, S., DeLeon, C., Braz, J. M., Craik, V., Kang, H. J., Wan, X., Huang, X., Billesbølle, C. B., Liu, Y., Che, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43506-6 · Cited by 43 · see citing works
“The docking parameters were optimized by performing control calculations 38 wherein performance was judged by ability to enrich a set of 21 known EP4 receptor antagonists, extracted from the IUPHAR 69 and CHEMBL 42 databases, versus 658 property-matched decoys generated using the…”
InfluentialSelf-citationWang, Q., Meng, F., Xie, Y., Wang, W., Meng, Y., Li, L., Liu, T., Qi, J., Ni, X., Zheng, S., et al. (2023) · ACS Central Science · doi:10.1021/acscentsci.2c01190 · Cited by 39 · see citing works
Min, Y., Ye, W., Wang, P., Wang, X., Li, H., Wu, N., Bauer, S., Zheng, S., Shi, Y., Wang, Y., et al. (2024) · Advanced Science · doi:10.1002/advs.202405404 · Cited by 40 · see citing works
“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 37 · see citing works
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 43 · see citing works
“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 42 · see citing works
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 36 · see citing works
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 35 · see citing works
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 34 · see citing works
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 · see citing works
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 32 · see citing works
“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 · see citing works
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 31 · see citing works
“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 30 · see citing works
Park, M., Baek, S., Park, S., Yi, J.-M., & Cha, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad344 · Cited by 31 · see citing works
“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 29 · see citing works
“…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 29 · see citing works
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 · see citing works
“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 28 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 27 · see citing works
“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 27 · see citing works
Gridnev, A., Maity, S., & Misra, J. R. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1021823 · Cited by 27 · see citing works
“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 27 · see citing works
“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 26 · see citing works
“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., & Bedem, H. V. D. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1171143 · Cited by 26 · see citing works
“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 25 · see citing works
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 25 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
“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 24 · see citing works
“…[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 25 · see citing works
“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 24 · see citing works
“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 24 · see citing works
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 · see citing works
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 23 · see citing works
“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 · see citing works
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 23 · see citing works
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Health Sciences › Medicine › Pharmacology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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