Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Open accessAbstract
A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set that includes more diverse targets such as GPCRs and ion channels, totaling 102 proteins with 22886 clustered ligands drawn from ChEMBL, each with 50 property-matched decoys drawn from ZINC. To ensure chemotype diversity, we cluster each target's ligands by their Bemis-Murcko atomic frameworks. We add net charge to the matched physicochemical properties and include only the most dissimilar decoys, by topology, from the ligands. An online automated tool (http://decoys.docking.org) generates these improved matched decoys for user-supplied ligands. We test this data set by docking all 102 targets, using the results to improve the balance between ligand desolvation and electrostatics in DOCK 3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org.
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DUD-E is an enhanced version of the original DUD set and has been widely used to train and validate scoring functions.
“The DUD-E dataset is an enhanced version of the original DUD set and has been widely used to train and validate scoring functions (Huang et al., 2006; Mysinger et al., 2012).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Mysinger et al., 2012
DUD-E is composed of 102 targets with corresponding active, inactive, marginal, and decoy compounds.
“It is composed of 102 targets with corresponding active, inactive, marginal, and decoy compounds.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Mysinger et al., 2012
PDBbind, DUD-E, and DEKOIS 2.0 are examples of widely used benchmarks for evaluating scoring functions.
“PDBbind (Liu et al., 2015), DUD-E (Mysinger et al., 2012), and DEKOIS 2.0 (Bauer et al., 2013) are examples of widely used benchmarks for evaluating scoring functions.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Mysinger et al., 2012
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Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,535 · see citing works
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382 · see citing works
Docking and scoring in virtual screening for drug discovery: methods and applications
Kitchen, D. B., Decornez, H. Y., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,245 · see citing works
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058 · see citing works
The Properties of Known Drugs. 1. Molecular Frameworks
Bemis, G. W., & Murcko, M. A. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm9602928 · Cited by 2,501 · see citing works
Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A., & Stahl, M. T. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100031x · Cited by 1,635 · see citing works
Benchmarking sets for molecular docking.
Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318 · see citing works
Comparison of Shape-Matching and Docking as Virtual Screening Tools
Hawkins, P. C. D., Skillman, A. G., & Nicholls, A. O. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0603365 · Cited by 998 · see citing works
The maximal affinity of ligands
Kuntz, I. D., Chen, K., Sharp, K. A., & Kollman, P. A. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.9997 · Cited by 859 · see citing works
Gohlke, H., & Klebe, G. (2002) · Angewandte Chemie International Edition · doi:10.1002/1521-3773(20020802)41:15 · Cited by 843 · see citing works
Bissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740 · see citing works
Comparative evaluation of eight docking tools for docking and virtual screening accuracy
Kellenberger, E., Rodrigo, J., Muller, P., & Rognan, D. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20149 · Cited by 577 · see citing works
Detailed Analysis of Scoring Functions for Virtual Screening
Ståhl, M., & Rarey, M. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm0003992 · Cited by 490 · see citing works
Assessing Scoring Functions for Protein−Ligand Interactions
Ferrara, P., Gohlke, H., Price, D. J., Klebe, G., & Brooks, C. L. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030489h · Cited by 481 · see citing works
Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data
Rohrer, S. G., & Baumann, K. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002649 · Cited by 403 · see citing works
Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment
Verdonk, M. L., Berdini, V., Hartshorn, M. J., Mooij, W. T. M., Murray, C. W., Taylor, R. D., & Watson, P. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034289q · Cited by 379 · see citing works
Docking and scoring with ICM: the benchmarking results and strategies for improvement
Neves, M., Totrov, M., & Abagyan, R. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9547-0 · Cited by 357 · see citing works
Recommendations for evaluation of computational methods
Jain, A. N., & Nicholls, A. O. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9196-5 · Cited by 350 · see citing works
Rapid Context-Dependent Ligand Desolvation in Molecular Docking
Mysinger, M. M., & Shoichet, B. K. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100214a · Cited by 323 · see citing works
Ligand discovery from a dopamine D3 receptor homology model and crystal structure
Carlsson, J., Coleman, R. G., Setola, V., Irwin, J. J., Fan, H., Schlessinger, A., Šali, A., Roth, B. L., & Shoichet, B. K. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.662 · Cited by 305 · see citing works
Automated Docking Screens: A Feasibility Study
Irwin, J. J., Shoichet, B. K., Mysinger, M. M., Huang, N., Colizzi, F., Wassam, P., & Cao, Y. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm9006966 · Cited by 288 · see citing works
Structure-based discovery of β 2 -adrenergic receptor ligands
Kolb, P., Rosenbaum, D. M., Irwin, J. J., Fung, J. J., Kobilka, B. K., & Shoichet, B. K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812657106 · Cited by 288 · see citing works
Surflex-Dock: Docking benchmarks and real-world application
Spitzer, R., & Jain, A. N. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9533-y · Cited by 284 · see citing works
Complementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain Inhibitors
Ferreira, R. S., Simeonov, A. M., Jadhav, A., Eidam, O., Mott, B. T., Keiser, M. J., McKerrow, J. H., Maloney, D. J., Irwin, J. J., & Shoichet, B. K. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100488w · Cited by 253 · see citing works
Structure-Based Discovery of A 2A Adenosine Receptor Ligands
Carlsson, J., Yoo, L. S., Gao, Z.-G., Irwin, J. J., Shoichet, B. K., & Jacobson, K. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100240h · Cited by 229 · see citing works
Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test
Liebeschuetz, J. W., Cole, J. C., & Korb, O. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9551-4 · Cited by 183 · see citing works
Good, A. C., & Oprea, T. I. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9167-2 · Cited by 180 · see citing works
DecoyFinder: an easy-to-use python GUI application for building target-specific decoy sets
Cereto‐Massagué, A., Guasch, L., Valls, C., Mulero, M., Pujadas, G., & Garcı́a-Vallvé, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts249 · Cited by 180 · 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
Grüneberg, S., Stubbs, M. T., & Klebe, G. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm011112j · Cited by 172 · see citing works
Community benchmarks for virtual screening
Irwin, J. J. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9189-4 · Cited by 171 · see citing works
How to do an evaluation: pitfalls and traps
Hawkins, P. C. D., Warren, G. L., Skillman, A. G., & Nicholls, A. O. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9166-3 · Cited by 166 · see citing works
Bias, reporting, and sharing: computational evaluations of docking methods
Jain, A. N. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9151-x · Cited by 159 · see citing works
E-MSD: an integrated data resource for bioinformatics
Golovin, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh078 · Cited by 159 · see citing works
Repasky, M. P., Murphy, R. B., Banks, J. L., Greenwood, J. R., Tubert‐Brohman, I., Bhat, S., & Friesner, R. A. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9575-9 · Cited by 158 · see citing works
Evaluation of DOCK 6 as a pose generation and database enrichment tool
Brozell, S. R., Mukherjee, S., Balius, T. E., Roe, D. R., Case, D. A., & Rizzo, R. C. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9565-y · Cited by 157 · see citing works
Schneider, N., Hindle, S. A., Lange, G., Klein, R., Albrecht, J., Briem, H., Beyer, K., Claußen, H., Gastreich, M., Lemmen, C., et al. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9531-0 · Cited by 149 · see citing works
Parameter Estimation for Scoring Protein−Ligand Interactions Using Negative Training Data
Pham, T. A., & Jain, A. N. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050040j · Cited by 145 · see citing works
Molecular Docking Screens Using Comparative Models of Proteins
Fan, H., Irwin, J. J., Webb, B. M., Klebe, G., Shoichet, B. K., & Sali, A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9003706 · Cited by 135 · see citing works
Structure-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 · see citing works
Structure-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 · see citing works
Ligand and Decoy Sets for Docking to G Protein-Coupled Receptors
Gatica, E. A., & Cavasotto, C. N. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200412p · Cited by 106 · see citing works
Graves, A. P., Brenk, R., & Shoichet, B. K. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm0491187 · Cited by 105 · 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
Can we use docking and scoring for hit-to-lead optimization?
Enyedy, I. J., & Egan, W. J. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9165-4 · Cited by 97 · see citing works
Vogel, S., Bauer, M. R., & Boeckler, F. M. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2001549 · Cited by 94 · see citing works
E-MSD: an integrated data resource for bioinformatics
Velankar, S. S. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki058 · Cited by 77 · see citing works
Tondi, D., Morandi, F., Bonnet, R., Costi, M. P., & Shoichet, B. K. (2005) · Journal of the American Chemical Society · doi:10.1021/ja042984o · Cited by 64 · see citing works
Better than Random? The Chemotype Enrichment Problem
Mackey, M. D., & Melville, J. L. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8003978 · Cited by 60 · see citing works
Virtual Decoy Sets for Molecular Docking Benchmarks
Wallach, I., & Lilien, R. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci100374f · Cited by 58 · see citing works
Lead Finder docking and virtual screening evaluation with Astex and DUD test sets
Novikov, F. N., Stroylov, V. S., Zeifman, A. A., Stroganov, O. V., Kulkov, V., & Chilov, G. G. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9549-y · Cited by 42 · see citing works
Ripphausen, P., Wassermann, A. M., & Bajorath, J. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200309j · Cited by 22 · see citing works
How to Benchmark Methods for Structure-Based Virtual Screening of Large Compound Libraries
Christofferson, A. J., & Huang, N. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-465-0_13 · Cited by 6 · see citing works
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AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings
Eberhardt, J., Santos-Martins, D., Tillack, A., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 7,111 · see citing works
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224 · see citing works
Molecular Docking: Shifting Paradigms in Drug Discovery
Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,450 · see citing works
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631 · see citing works
Ultra-large library docking for discovering new chemotypes
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998 · see citing works
Comparative Assessment of Scoring Functions: The CASF-2016 Update
Su, M., Yang, Q., Du, Y., Feng, G., Liu, Z., Li, Y., & Wang, R. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00545 · Cited by 838 · see citing works
DOCK 6: Impact of New Features and Current Docking Performance
Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S., Moustakas, D., Lang, P. T., Case, D., Kuntz, I., & Rizzo, R. C. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23905 · Cited by 731 · see citing works
Protein–Ligand Scoring with Convolutional Neural Networks
Ragoza, M., Hochuli, J. E., Idrobo, E., Sunseri, J., & Koes, D. R. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00740 · Cited by 835 · see citing works
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809 · see citing works
Machine learning unifies the modeling of materials and molecules
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764 · see citing works
ProTox: a web server for the in silico prediction of rodent oral toxicity
Drwal, M. N., Banerjee, P., Dunkel, M., Wettig, M. R., & Preißner, R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku401 · Cited by 742 · see citing works
Tsubaki, M., Tomii, K., & Sese, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty535 · Cited by 688 · see citing works
Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 670 · see citing works
rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids
Carmona, S. R., Álvarez-García, D., Foloppe, N., Garmendia-Doval, A. B., Juhos, S., Schmidtke, P., Barril, X., Hubbard, R. E., & Morley, S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003571 · Cited by 645 · see citing works
Sabe, V. T., Ntombela, T., Jhamba, L. A., Maguire, G. E. M., Govender, T., Naicker, T., & Kruger, H. G. (2021) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.113705 · Cited by 631 · see citing works
Use of molecular docking computational tools in drug discovery
Stanzione, F., Giangreco, I., & Cole, J. C. (2021) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2021.01.004 · Cited by 649 · see citing works
Chen, L., Tan, X., Wang, D., Zhong, F., Liu, X., Yang, T., Luo, X., Chen, K., Jiang, H., & Zheng, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa524 · Cited by 607 · see citing works
Structure-Based Virtual Screening: From Classical to Artificial Intelligence
Maia, E. H. B., de Assis, L. C., de Oliveira, T. A., da Silva, A. M., & Taranto, A. G. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00343 · Cited by 591 · see citing works
A practical guide to large-scale docking
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., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00597-z · Cited by 546 · see citing works
“77 Rerun the dielectric boundary modifying sphere scan in Steps 65 – 68.”
BackgroundMethodInfluentialRifaioğlu, A. S., Ataş, H., Martin, M. J., Çetin-Atalay, R., Atalay, V., & Doğan, T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby061 · Cited by 535 · see citing works
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Cśanyi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 555 · see citing works
Ton, A., Gentile, F., Hsing, M., Ban, F., & Cherkasov, A. R. (2020) · Molecular Informatics · doi:10.1002/minf.202000028 · Cited by 518 · see citing works
Wallach, I., Dzamba, M., & Heifets, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.02855 · Cited by 698 · see citing works
Forging the basis for developing protein-ligand interaction scoring functions.
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., & Wang, R. (2017) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00491 · Cited by 493 · see citing works
Structure of the D2 dopamine receptor bound to the atypical antipsychotic drug risperidone
Wang, S., Che, T., Levit, A., Shoichet, B. K., Wacker, D., & Roth, B. L. (2018) · Nature · doi:10.1038/nature25758 · Cited by 487 · see citing works
Crystal Structure of an LSD-Bound Human Serotonin Receptor
Wacker, D., Wang, S., McCorvy, J. D., Betz, R. M., Venkatakrishnan, A., Levit, A., Lansu, K., Schools, Z. L., Che, T., Nichols, D. E., et al. (2017) · Cell · doi:10.1016/j.cell.2016.12.033 · Cited by 458 · see citing works
Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
Gentile, F., Agrawal, V., Hsing, M., Ton, A., Ban, F., Norinder, U., Gleave, M. E., & Cherkasov, A. R. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00229 · Cited by 455 · see citing works
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A., Ortner, C., Cśanyi, G., Ceriotti, M., et al. (2021) · Cambridge University Engineering Department Publications Database · Cited by 440 · see citing works
Pharmit: interactive exploration of chemical space
Sunseri, J., & Koes, D. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw287 · Cited by 426 · see citing works
Graph convolutional networks for computational drug development and discovery
Sun, M., Zhao, S., Gilvary, C. M., Elemento, O., Zhou, J., & Wang, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz042 · Cited by 414 · see citing works
Lim, J., Ryu, S., Park, K., Choe, Y. J., Ham, J., & Kim, W. Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00387 · Cited by 403 · see citing works
Drug Design by Pharmacophore and Virtual Screening Approach
Giordano, D., Biancaniello, C., Argenio, M. A., & Facchiano, A. M. (2022) · Pharmaceuticals · doi:10.3390/ph15050646 · Cited by 410 · see citing works
Machine-learning methods for ligand–protein molecular docking
Crampon, K., Giorkallos, A., Deldossi, M., Baud, S., & Steffenel, L. A. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.09.007 · Cited by 406 · see citing works
Li, Y., Han, L., Liu, Z., & Wang, R. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500081m · Cited by 384 · see citing works
Shivanika, C., Deepak Kumar, S., Ragunathan, V., Tiwari, P., Sumitha, A., & Devi, P. B. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1815584 · Cited by 377 · see citing works
Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
Torng, W., & Altman, R. B. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00628 · Cited by 371 · see citing works
Performance of machine-learning scoring functions in structure-based virtual screening.
Wójcikowski, M., Ballester, P. J., & Siedlecki, P. (2017) · Scientific Reports · doi:10.1038/srep46710 · Cited by 364 · see citing works
Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations
Guterres, H., & Im, W. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00057 · Cited by 361 · see citing works
Boosting docking-based virtual screening with deep learning.
Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395 · see citing works
Francoeur, P. G., Masuda, T., Sunseri, J., Jia, A., Iovanisci, R. B., Snyder, I. M., & Koes, D. R. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00411 · Cited by 353 · see citing works
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331 · see citing works
Sieg, J., Flachsenberg, F., & Rarey, M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00712 · Cited by 323 · see citing works
Predicting drug–protein interaction using quasi-visual question answering system
Zheng, S., Li, Y., Chen, S., Xu, J., & Yang, Y. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0152-y · Cited by 310 · see citing works
Opo, F. A. D. M., Rahman, M. M., Ahammad, F., Ahmed, I., Bhuiyan, M. A., & Asiri, A. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-83626-x · Cited by 310 · see citing works
Integrating Molecular Docking and Molecular Dynamics Simulations
Santos, L. H., Ferreira, R. S., & Caffarena, E. R. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9752-7_2 · Cited by 300 · see citing works
Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization
Wallach, I., & Heifets, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00403 · Cited by 287 · see citing works
Chen, L., Cruz, A., Ramsey, S., Dickson, C. J., Duca, J. S., Horn̆ák, V., Koes, D. R., & Kurtzman, T. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220113 · Cited by 291 · see citing works
Covalent docking of large libraries for the discovery of chemical probes
London, N., Miller, R. M., Krishnan, S., Uchida, K., Irwin, J. J., Eidam, O., Gibold, L., Cimermančič, P., Bonnet, R., Shoichet, B. K., et al. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1666 · Cited by 274 · see citing works
An overview of molecular fingerprint similarity search in virtual screening
Muegge, I., & Mukherjee, P. K. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2016.1117070 · Cited by 278 · see citing works
Docking Screens for Novel Ligands Conferring New Biology
Irwin, J. J., & Shoichet, B. K. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b02008 · Cited by 281 · see citing works
Benchmarking 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
Next generation 3D pharmacophore modeling
Schaller, D., Šribar, D., Noonan, T., Deng, L., Nguyen, T. N., Pach, S., Machalz, D., Bermúdez, M., & Wolber, G. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1468 · Cited by 276 · see citing works
The Light and Dark Sides of Virtual Screening: What Is There to Know?
Gimeno, A., Ojeda, M. J., Tomas‐Hernández, S., Cereto‐Massagué, A., Beltrán‐Debón, R., Mulero, M., Pujadas, G., & Garcı́a-Vallvé, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20061375 · Cited by 270 · see citing works
Jiang, D., Hsieh, C., Wu, Z., Kang, Y., Wang, J., Wang, E., Liao, B., Shen, C., Xu, L., Wu, J. F., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01830 · Cited by 260 · see citing works
Improvements, trends, and new ideas in molecular docking: 2012–2013 in review
Yuriev, E., Holien, J. K., & Ramsland, P. A. (2015) · Journal of Molecular Recognition · doi:10.1002/jmr.2471 · Cited by 264 · see citing works
LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening
Tran‐Nguyen, V., Jacquemard, C., & Rognan, D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00155 · Cited by 260 · see citing works
Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity
Kaplan, A. L., Confair, D. N., Kim, K., Barros-Álvarez, X., Rodriguiz, R. M., Ying, Y., Kweon, O. S., Che, T., McCorvy, J. D., Kamber, D. N., et al. (2022) · Nature · doi:10.1038/s41586-022-05258-z · Cited by 249 · see citing works
A Guide to In Silico Drug Design
Chang, Y., Hawkins, B. A., Du, J. J., Groundwater, P. W., Hibbs, D. E., & Lai, F. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics15010049 · Cited by 280 · see citing works
Vázquez, J., López, M., Gibert, E., Herrero, E., & Luque, F. J. (2020) · Molecules · doi:10.3390/molecules25204723 · Cited by 253 · see citing works
MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities
Li, S., Wan, F., Shu, H., Jiang, T., Zhao, D. Y., & Zeng, J. (2020) · Cell Systems · doi:10.1016/j.cels.2020.03.002 · Cited by 245 · see citing works
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Shen, C., Ding, J., Wang, Z., Cao, D., Ding, X., & Hou, T. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1429 · Cited by 245 · see citing works
Feinstein, W. P., & Bryliński, M. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0067-5 · Cited by 245 · 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
Rifaioğlu, A. S., Nalbat, E., Atalay, V., Martin, M. J., Çetin-Atalay, R., & Doğan, T. (2020) · Chemical Science · doi:10.1039/c9sc03414e · Cited by 241 · see citing works
Shape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229 · see citing works
Ligand Pose and Orientational Sampling in Molecular Docking
Coleman, R. G., Carchia, M., Sterling, T., Irwin, J. J., & Shoichet, B. K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075992 · Cited by 223 · see citing works
Machine learning classification can reduce false positives in structure-based virtual screening
Adeshina, Y. O., Deeds, E. J., & Karanicolas, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2000585117 · Cited by 225 · see citing works
New machine learning and physics-based scoring functions for drug discovery
Guedes, I. A., Barreto, A. M. S., Marinho, D., Krempser, E., Kuenemann, M. A., Spérandio, O., Dardenne, L. E., & Miteva, M. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-82410-1 · Cited by 215 · see citing works
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212 · see citing works
“In addition to natural products, we compiled a non-redundant set of mostly synthetic bioactive compounds from the Data-base of Useful Decoys, Extended (DUD-E) database [50] by selecting 17,499 active molecules against 101 pharmacologically relevant targets.”
BackgroundGetting the most out of PubChem for virtual screening
Kim, S. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1216967 · Cited by 218 · see citing works
1001 Ways to run AutoDock Vina for virtual screening
Jaghoori, M. M., Bleijlevens, B., & Olabarriaga, S. D. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9900-9 · Cited by 213 · see citing works
Kaserer, T., Beck, K. R., Akram, M., Odermatt, A., & Schuster, D. (2015) · Molecules · doi:10.3390/molecules201219880 · Cited by 208 · see citing works
Deep Learning in Virtual Screening: Recent Applications and Developments
Kimber, T. B., Chen, Y., & Volkamer, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094435 · Cited by 213 · see citing works
Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
Acharya, A., Agarwal, R., Baker, M. B., Baudry, J. Y., Bhowmik, D., Boehm, S., Byler, K., Chen, S., Coates, L., Cooper, C. J., et al. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01010 · Cited by 204 · see citing works
How good are AlphaFold models for docking-based virtual screening?
Scardino, V., Di Filippo, J. I., & Cavasotto, C. N. (2023) · iScience · doi:10.1016/j.isci.2022.105920 · Cited by 207 · see citing works
SLC transporters: structure, function, and drug discovery
Colas, C., Ung, P. M. U., & Schlessinger, A. (2016) · MedChemComm · doi:10.1039/c6md00005c · Cited by 201 · see citing works
A critical overview of computational approaches employed for COVID-19 drug discovery
Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01065k · Cited by 206 · see citing works
Bauer, M. R., Ibrahim, T. M., Vogel, S. M., & Boeckler, F. M. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400115b · Cited by 200 · see citing works
Carpenter, K. A., & Huang, X. (2018) · Current Pharmaceutical Design · doi:10.2174/1381612824666180607124038 · Cited by 203 · see citing works
Singh, R., Sledzieski, S., Bryson, B. D., Cowen, L., & Berger, B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220778120 · Cited by 207 · see citing works
Protein–Ligand Docking in the Machine-Learning Era
Yang, C., Chen, E. A., & Zhang, Y. (2022) · Molecules · doi:10.3390/molecules27144568 · Cited by 206 · see citing works
Application of MM-PBSA Methods in Virtual Screening
Poli, G., Granchi, C., Rizzolio, F., & Tuccinardi, T. (2020) · Molecules · doi:10.3390/molecules25081971 · Cited by 194 · see citing works
A Comprehensive Docking and MM/GBSA Rescoring Study of Ligand Recognition upon Binding Antithrombin
Zhang, X., Pérez‐Sánchez, H., & Lightstone, F. C. (2017) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026616666161117112604 · Cited by 192 · see citing works
Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
Palacio‐Rodríguez, K., Lans, I., Cavasotto, C. N., & Cossio, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-41594-3 · Cited by 186 · see citing works
Shen, C., Zhang, X., Deng, Y., Gao, J., Wang, D., Xu, L., Pan, P., Hou, T., & Kang, Y. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00991 · Cited by 179 · see citing works
Evaluation guidelines for machine learning tools in the chemical sciences
Bender, A., Schneider, N., Segler, M., Walters, W. P., Engkvist, O., & Rodrigues, T. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00391-9 · Cited by 186 · see citing works
Graph neural network approaches for drug-target interactions
Zhang, Z., Chen, L., Zhong, F., Wang, D., Jiang, J., Zhang, S., Jiang, H., Zheng, M., & Li, X. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.102327 · Cited by 184 · see citing works
Han, R., Yoon, H., Kim, G., Lee, H., & Lee, Y. (2023) · Pharmaceuticals · doi:10.3390/ph16091259 · Cited by 192 · see citing works
Da, C., & Kireev, D. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500319f · Cited by 172 · see citing works
GNINA 1.3: the next increment in molecular docking with deep learning
McNutt, A. T., Li, Y., Meli, R., Aggarwal, R., & Koes, D. R. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00973-x · Cited by 172 · see citing works
Machine‐learning scoring functions for structure‐based virtual screening
Li, H., Sze, K., Lü, G., & Ballester, P. J. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1478 · Cited by 167 · see citing works
Lino, C. I., de Souza, I. G., Borelli, B. M., Matos, T. T. S., Teixeira, I. N. S., Ramos, J. P., de Souza-Fagundes, E. M., Fernandes, P. O., Maltarollo, V. G., Johann, S., et al. (2018) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2018.03.083 · Cited by 162 · see citing works
Kolšek, K., Mavri, J., Dolenc, M. S., Gobec, S., & Turk, S. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400649p · Cited by 160 · see citing works
Ballante, F., Kooistra, A. J., Kampen, S., de Graaf, C., & Carlsson, J. (2021) · Pharmacological Reviews · doi:10.1124/pharmrev.120.000246 · Cited by 160 · see citing works
Encoding Protein–Ligand Interaction Patterns in Fingerprints and Graphs
Desaphy, J., Raimbaud, E., Ducrot, P., & Rognan, D. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300566n · Cited by 159 · see citing works
Meli, R., Morris, G. M., & Biggin, P. C. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.885983 · Cited by 164 · see citing works
Machine learning in computational docking
Khamis, M. A., Gomaa, W. E., & Ahmed, W. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.02.002 · Cited by 156 · see citing works
Quantum machine learning for chemistry and physics
Sajjan, M., Li, J., Selvarajan, R., Sureshbabu, S. H., Kale, S. S., Gupta, R., Singh, V., & Kais, S. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00203e · Cited by 157 · see citing works
- Authors
- Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Irwin, John. J.
- Shoichet, Brian K.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-07-05
- Volume
- 55
- Issue
- 14
- Pages
- 6582-6594
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- PMC
- PMC3405771
- OpenAlex
- W1968319881
- Semantic Scholar
- 3b2ffd1b7ce968924e9046b0a0ff3a1f2b033de9
- Cited by
- 2,412
- References
- 54
- Citations by year
- 2026: 231 · 2025: 285 · 2024: 283 · 2023: 243 · 2022: 279 · 2021: 247 · 2020: 207 · 2019: 162 · 2018: 123 · 2017: 96 · 2016: 101 · 2015: 77 · 2014: 44 · 2013: 28 · 2012: 3
- FWCI
- 52.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Medicinal Chemistry (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- DUD-E, decoy generation, G protein-coupled receptors, ion channels, ligand clustering, net charge, electrostatics, ChEMBL, zinc, decoy database
- MeSH terms
- Humans, Ligands, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Reference Standards, Cluster Analysis, Benchmarking, Drug Discovery
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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