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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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Generating Multibillion Chemical Space of Readily Accessible Screening Compounds
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“Atomic desolvation energies for ligand atoms are calculated during ligand building using AMSOL 111 and are included in the ligand conformer library.”
MethodSelf-citationKinase Inhibitors: Not Just for Kinases Anymore
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BackgroundLigand Pose and Orientational Sampling in Molecular Docking
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Stoichiometry and Physical Chemistry of Promiscuous Aggregate-Based Inhibitors
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Structures 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
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 205
Self-citationPromiscuous 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 204
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 202
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×
“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
Bender, A., & Glen, R. C. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0500177 · Cited by 180
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 180
Self-citationBottegoni, G., Kufareva, I., Totrov, M. M., & Abagyan, R. A. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8009958 · Cited by 179
Recipes 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 177
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
Colloidal 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 171
Self-citationSmall-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×
“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 .”
BackgroundCavasotto, 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
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×
“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 149
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 143 · cited 15×
“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-citationKatritch, 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
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×
“Electrostatic potentials within the binding pocket are estimated by numerically solving the Poisson – Boltzmann equation using QNIFFT 16,110 .”
MethodIdentification 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 133
Thorne, 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
Exploiting Ordered Waters in Molecular Docking
Huang, N., & Shoichet, B. K. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8006239 · Cited by 133 · cited 2×
“Consequently, these scoring grids can be fi ne-tuned by modulating the protein – solvent dielectric interface (see below Steps 41 – 44).”
BackgroundSelf-citationDocking Screens: Right for the Right Reasons?
Kolb, P., & Irwin, J. J. (2009) · Current Topics in Medicinal Chemistry · doi:10.2174/156802609789207091 · Cited by 131
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 131 · cited 12×
“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-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×
“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×
“…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×
“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
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×
“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-citationAllen, W. J., & Rizzo, R. C. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400534h · Cited by 107 · cited 1×
“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 .”
BackgroundPredicting 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
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
Self-citationThe Chemical Basis of Pharmacology
Keiser, M. J., Irwin, J. J., & Shoichet, B. K. (2010) · Biochemistry · doi:10.1021/bi101540g · Cited by 96 · cited 1×
“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×
“…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 90
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×
“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
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 78
Self-citationVirtual 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 73
Mahmoud, A. H., Masters, M. R., Ying, Y., & Lill, M. A. (2020) · Communications Chemistry · doi:10.1038/s42004-020-0261-x · Cited by 73
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 71
Weichenberger, C. X., & Sippl, M. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm263 · Cited by 64
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
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
Ligand 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 62
Self-citationLigand-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 59 · cited 1×
“…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…”
BackgroundKolb, 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×
“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
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×
“…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 48
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 44
Jaiteh, M., Rodríguez‐Espigares, I., Selent, J., & Carlsson, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007680 · Cited by 42 · cited 2×
“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-citationBrajesh, 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 39
Colloidal 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
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
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 36
Martí-Solano, M., Schmidt, D., Kolb, P., & Selent, J. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.01.009 · Cited by 32
Daga, P. R., Polgar, W. E., & Zaveri, N. T. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500291a · Cited by 28
Wu, Y., Lou, L., & Xie, Z. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00081 · Cited by 24 · cited 1×
“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
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×
“…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
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
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 16
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Bandyopadhyay, D., Bhatnagar, A., Jain, S., & Pratyaksh, P. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c02629 · Cited by 15
Research works printed but not linked: 2
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Cited by
Sorted by citations
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
“%) 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 546
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
“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 451
“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
“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).”
BackgroundMethodInfluential
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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