Benchmarking data sets for the evaluation of virtual ligand screening methods: review and perspectives.
Open accessAbstract
Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The use of decoys instead of validated inactive compounds remains a major drawback for most datasets since no experimental activity is reported for them and confirmed inactive molecules are scarce.
“The use of decoys instead of validated inactive compounds remains a major drawback for most datasets since no experimental activity are reported for them, and the number of confirmed inactive molecules is too scarce (Lagarde et al., 2015; Chaput et al., 2016b; Réau et al., 2018).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Lagarde et al., 2015
Cites
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763 · 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
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
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566 · see citing works
A Fast Flexible Docking Method using an Incremental Construction Algorithm
Rarey, M., Kramer, B., Lengauer, T., & Klebe, G. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0477 · Cited by 2,836 · see citing works
Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412 · see citing works
A geometric approach to macromolecule-ligand interactions
Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982) · Journal of Molecular Biology · doi:10.1016/0022-2836(82)90153-x · Cited by 2,287 · see citing works
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl999 · Cited by 2,145 · see citing works
Eldridge, M., Murray, C. W., Auton, T. R., Paolini, G. V., & Mee, R. P. (1997) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1007996124545 · Cited by 1,692 · see citing works
Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine
Jain, A. N. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm020406h · Cited by 1,351 · 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
PubChem: Integrated Platform of Small Molecules and Biological Activities
Bolton, E., Wang, Y., Thiessen, P. A., & Bryant, S. H. (2008) · Annual reports in computational chemistry · doi:10.1016/s1574-1400(08)00012-1 · Cited by 1,267 · see citing works
Wang, R., Fang, X., Lu, Y., & Wang, S. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030580l · Cited by 1,137 · see citing works
A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
Muegge, I., & Martin, Y. C. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm980536j · Cited by 1,008 · see citing works
Integration of virtual and high-throughput screening
Bajorath, J. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd941 · Cited by 935 · see citing works
DOCK 6: Combining techniques to model RNA–small molecule complexes
Lang, P. T., Brozell, S. R., Mukherjee, S., Pettersen, E. F., Meng, E. C., Thomas, V., Rizzo, R. C., Case, D. A., James, T. L., & Kuntz, I. D. (2009) · RNA · doi:10.1261/rna.1563609 · Cited by 747 · see citing works
Bissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740 · see citing works
Prediction of Protein−Ligand Interactions. Docking and Scoring: Successes and Gaps
Leach, A. R., Shoichet, B. K., & Peishoff, C. E. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm060999m · Cited by 648 · see citing works
Ant Colony Optimization and Swarm Intelligence
Dorigo, M., Stützle, T., Poli, R., Martinoli, A., Gambardella, L. M., & Birattari, M. (2006) · Lecture notes in computer science · doi:10.1007/11839088 · Cited by 529 · see citing works
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707515 · Cited by 514 · see citing works
The cost of drug development: a systematic review.
Morgan, S. G., Grootendorst, P., Lexchin, J., Cunningham, C., & Greyson, D. L. (2011) · Health Policy · doi:10.1016/j.healthpol.2010.12.002 · Cited by 510 · see citing works
McGann, M. R., Almond, H. R., Nicholls, A. O., Grant, J., & Brown, F. K. (2002) · Biopolymers · doi:10.1002/bip.10207 · Cited by 493 · 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
Doman, T. N., McGovern, S. L., Witherbee, B. J., Kasten, T. P., Kurumbail, R. G., Stallings, W. C., Connolly, D. T., & Shoichet, B. K. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm010548w · Cited by 463 · see citing works
Fang, H., Tong, W., Shi, L. M., Blair, R., Perkins, R., Branham, W. S., Hass, B. S., Xie, Q., Dial, S. L., Moland, C. L., et al. (2001) · Chemical Research in Toxicology · doi:10.1021/tx000208y · Cited by 449 · see citing works
Böhm, M., Stürzebecher, J., & Klebe, G. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm981062r · Cited by 440 · see citing works
Kirchmair, J., Markt, P., Distinto, S., Wolber, G., & Langer, T. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9163-6 · Cited by 423 · 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
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
Hennequin, L. F., Stokes, E. S. E., Thomas, A. P., Johnstone, C., Plé, P. A., Ogilvie, D., Dukes, M., Wedge, S. R., Kendrew, J., & Curwen, J. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm011022e · Cited by 311 · see citing works
PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design
Korb, O., Stützle, T., & Exner, T. E. (2006) · Lecture notes in computer science · doi:10.1007/11839088_22 · Cited by 288 · see citing works
McGovern, S. L., & Shoichet, B. K. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0300330 · Cited by 276 · see citing works
Structure-Activity Relationships in Purine-Based Inhibitor Binding to HSP90 Isoforms
Wright, L. M., Barril, X., Dymock, B. W., Sheridan, L., Surgenor, A. E., Beswick, M., Drysdale, M., Collier, A. J., Massey, A., Davies, N., et al. (2004) · Chemistry & Biology · doi:10.1016/j.chembiol.2004.03.033 · Cited by 268 · see citing works
Sun, L., Tran, N., Liang, C., Tang, F. L. M., Rice, A. G., Schreck, R., Waltz, K., Shawver, L. K., McMahon, G., & Tang, C. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm9904295 · Cited by 261 · see citing works
Virtual Screening of Molecular Databases Using a Support Vector Machine
Jorissen, R. N., & Gilson, M. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049641u · Cited by 258 · see citing works
SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex
Wang, R., Liu, L., Lai, L., & Youqi, T. (1998) · Journal of Molecular Modeling · doi:10.1007/s008940050096 · Cited by 249 · see citing works
An ant colony optimization approach to flexible protein–ligand docking
Korb, O., Stützle, T., & Exner, T. E. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0006-9 · Cited by 246 · see citing works
Hennequin, L. F., Thomas, A. P., Johnstone, C., Stokes, E. S. E., Plé, P. A., Lohmann, J.-J. M., Ogilvie, D., Dukes, M., Wedge, S. R., Curwen, J., et al. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990345w · Cited by 244 · see citing works
A Comparison of Methods for Modeling Quantitative Structure−Activity Relationships
Sutherland, J. J., O’Brien, L. A., & Weaver, D. F. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0497141 · Cited by 230 · see citing works
Zandt, M. C. V., Jones, M. L., Gunn, D., Geraci, L. S., Jones, J. H., Sawicki, D. R., Sredy, J., JACOT, J. L., DiCioccio, A. T., Petrova, T. E., et al. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm0492094 · Cited by 230 · see citing works
Dymock, B. W., Barril, X., Brough, P. A., Cansfield, J. E., Massey, A. J., McDonald, E., Hubbard, R. E., Surgenor, A. E., Roughley, S. D., Webb, P., et al. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050355z · Cited by 227 · 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
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
The holistic integration of virtual screening in drug discovery
Tanrikulu, Y., Krüger, B., & Proschak, E. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.01.007 · Cited by 178 · 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
Rognan, D., Lauemøller, S. L., Holm, A., Buus, S., & Tschinke, V. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm9910775 · Cited by 165 · see citing works
Essential considerations for using protein–ligand structures in drug discovery
Warren, G. L., Do, T. D., Kelley, B. P., Nicholls, A. O., & Warren, S. D. (2012) · Drug Discovery Today · doi:10.1016/j.drudis.2012.06.011 · Cited by 161 · see citing works
Ealick, S. E., Babu, Y. S., Bugg, C. E., Erion, M. D., Guida, W. C., Montgomery, J. A. J., & Secrist, J. A. I. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.24.11540 · Cited by 154 · 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
Hierarchical Docking of Databases of Multiple Ligand Conformations
Lorber, D. M., & Shoichet, B. K. (2005) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026054637683 · Cited by 145 · see citing works
Improving Structure-Based Virtual Screening by Multivariate Analysis of Scoring Data
Jacobsson, M., Lidén, P., Stjernschantz, E., Boström, H., & Norinder, U. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm030896t · Cited by 126 · see citing works
Virtual 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 · 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
Kinases, Homology Models, and High Throughput Docking
Diller, D., & Li, R. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm020503a · Cited by 120 · see citing works
Fast Structure-Based Virtual Ligand Screening Combining FRED, DOCK, and Surflex
Miteva, M. A., Lee, W. H., Montès, M., & Villoutreix, B. O. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050262h · Cited by 111 · see citing works
Clark, R. D., & Webster-Clark, D. J. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9181-z · Cited by 110 · 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
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
Optimal assignment methods for ligand-based virtual screening
Jahn, A., Hinselmann, G., Fechner, N., & Zell, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-14 · Cited by 94 · see citing works
Perkins, E. L., Sun, D., Nguyen, A., Tulac, S., Francesco, M., Tavana, H., Nguyen, H., Tugendreich, S., Barthmaier, P., Couto, J., et al. (2001) · PubMed · Cited by 91 · see citing works
Vogt, M., Stumpfe, D., Geppert, H., & Bajorath, J. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100492z · Cited by 89 · see citing works
Estimating Error Rates in Bioactivity Databases
Tiikkainen, P., Bellis, L. J., Light, Y., & Franke, L. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400099q · Cited by 68 · see citing works
Virtual Screening for Kinase Targets
Muegge, I., & Enyedy, I. J. (2004) · Current Medicinal Chemistry · doi:10.2174/0929867043455684 · Cited by 65 · see citing works
Good, A. C., Hermsmeier, M. A., & Hindle, S. A. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-004-4067-1 · Cited by 65 · 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
Giganti, D. O., Guillemain, H., Spadoni, J., Nilges, M., Zagury, J., & Montès, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900507g · Cited by 63 · see citing works
Self-citationWang, J., Kang, X., Kuntz, I. D., & Kollman, P. A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049606e · Cited by 63 · 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
NRLiSt BDB, the Manually Curated Nuclear Receptors Ligands and Structures Benchmarking Database
Lagarde, N., Nasr, N. B., Jérémie, A., Guillemain, H., Laville, V., Labib, T., Zagury, J., & Montès, M. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm500132p · Cited by 57 · see citing works
Self-citationXia, J., Jin, H., Liu, Z., Zhang, L., & Wang, X. S. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500062f · Cited by 56 · see citing works
Benchmarking methods and data sets for ligand enrichment assessment in virtual screening
Xia, J., Tilahun, E. L., Reid, T.-E. R., Zhang, L., & Wang, X. S. (2014) · Methods · doi:10.1016/j.ymeth.2014.11.015 · Cited by 50 · see citing works
Tikhe, J. G., Webber, S. E., Hostomský, Z., Maegley, K. A., Ekkers, A., Li, J., Yu, X., Almassy, R. J., Kumpf, R. A., Boritzki, T. J., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030513r · Cited by 48 · see citing works
Development of KiBank, a database supporting structure-based drug design
Zhang, J., Aizawa, M., Amari, S., Iwasawa, Y., Nakano, T., & Nakata, K. (2004) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2004.09.003 · Cited by 48 · see citing works
Tuzun, R. E., Noid, D. W., & Sumpter, B. G. (1997) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(19971115)18:14 · Cited by 48 · see citing works
Protein Structures in Virtual Screening: A Case Study with CDK2
THOMAS, M. P., McInnes, C., & Fischer, P. M. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050554i · Cited by 43 · see citing works
Zhang, S., Tan, J., Lai, Z., Li, Y., Pang, J., Xiao, J., Huang, Z., Zhang, Y., Ji, H., & Lai, Y. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5002058 · Cited by 41 · see citing works
Varney, M. D., Palmer, C. L., Romines, W. H., Boritzki, T. J., Margosiak, S. A., Almassy, R. J., Janson, C. A., Bartlett, C. A., Howland, E. J., & Ferre, R. (1997) · Journal of Medicinal Chemistry · doi:10.1021/jm9607459 · Cited by 38 · see citing works
Nasr, N. B., Guillemain, H., Lagarde, N., Zagury, J., & Montès, M. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci3004557 · Cited by 35 · see citing works
Self-citationRohrer, S. G., & Baumann, K. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700099u · Cited by 34 · see citing works
Comparative Modeling and Benchmarking Data Sets for Human Histone Deacetylases and Sirtuin Families
Xia, J., Tilahun, E. L., Kebede, E. H., Reid, T.-E. R., Zhang, L., & Wang, X. S. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci5005515 · Cited by 27 · see citing works
Li‐Gao, R., Stumpfe, D., Vogt, M., Geppert, H., & Bajorath, J. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2003945 · Cited by 27 · see citing works
Kobayashi, M., Kinjo, T., Koseki, Y., Bourne, C. R., Barrow, W. W., & Aoki, S. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400686d · Cited by 27 · see citing works
Lindh, M., Svensson, F., Schaal, W., Zhang, J., Sköld, C., Brandt, P., & Karlén, A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci5005465 · Cited by 24 · 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
Balaji, B., Hariharan, S., Shah, D. B., & Ramanathan, M. (2014) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2014.09.005 · Cited by 20 · see citing works
Bobach, C., Tennstedt, S., Palberg, K., Denkert, A., Brandt, W., de Meijere, A., Seliger, B., & Wessjohann, L. A. (2014) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2014.11.026 · Cited by 18 · see citing works
Fan, F., Hu, R., Munzli, A., Chen, Y., Dunn, R. T., Weikl, K., Strauch, S., Schwandner, R., Afshari, C. A., Hamadeh, H. K., et al. (2015) · Toxicological Sciences · doi:10.1093/toxsci/kfv052 · Cited by 11 · see citing works
Lagarde, N., Zagury, J., & Montès, M. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500305c · Cited by 9 · see citing works
Self-citationLi‐Gao, R., & Bajorath, J. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9603-9 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Rifaioğlu, 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
Wallach, I., Dzamba, M., & Heifets, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.02855 · Cited by 698 · 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
Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations
Liu, K., Watanabe, E., & Kokubo, H. (2017) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-0005-2 · Cited by 272 · see citing works
Artificial intelligence in drug discovery: applications and techniques
Deng, J., Yang, Z., Ojima, I., Samaras, D., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab430 · Cited by 236 · 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
Yang, J., Shen, C., & Huang, N. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00069 · Cited by 124 · see citing works
Chaput, L., Martinez‐Sanz, J., Saettel, N., & Mouawad, L. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0167-x · Cited by 118 · see citing works
Decoys Selection in Benchmarking Datasets: Overview and Perspectives
Réau, M. F., Langenfeld, F., Zagury, J., Lagarde, N., & Montès, M. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00011 · Cited by 108 · see citing works
Self-citationGenerating property-matched decoy molecules using deep learning
Imrie, F., Bradley, A. R., & Deane, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab080 · Cited by 106 · see citing works
A 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
Computational chemistry in drug lead discovery and design
Cavasotto, C. N., Aucar, M. G., & Adler, N. S. (2018) · International Journal of Quantum Chemistry · doi:10.1002/qua.25678 · Cited by 83 · see citing works
Sun, H., Pan, P., Tian, S., Xu, L., Kong, X., Li, Y., Li, D., & Hou, T. (2016) · Scientific Reports · doi:10.1038/srep24817 · Cited by 79 · see citing works
Statistical and machine learning approaches to predicting protein–ligand interactions
Colwell, L. J. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.01.006 · Cited by 65 · see citing works
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 60 · see citing works
RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features
Holderbach, S., Adam, L., Jayaram, B., Wade, R. C., & Mukherjee, G. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.601065 · Cited by 43 · see citing works
GPCR-Bench: A Benchmarking Set and Practitioners’ Guide for G Protein-Coupled Receptor Docking
Weiss, D. R., Bortolato, A., Tehan, B. G., & Mason, J. S. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00660 · Cited by 36 · see citing works
Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery
Cruz‐Monteagudo, M., Schürer, S. C., Tejera, E., Pérez‐Castillo, Y., Medina‐Franco, J. L., Sánchez‐Rodríguez, A., & Borges, F. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.02.004 · Cited by 33 · see citing works
Damm‐Ganamet, K. L., Arora, N. R., Bécart, S., Edwards, J. P., Lebsack, A. D., McAllister, H., Nelen, M. I., Rao, N. L., Westover, L., Wiener, J. J. M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00941 · Cited by 31 · see citing works
Jiang, Y., Xiong, W., Jia, L., Xu, L., Cai, Y., Chen, Y., Jin, J., Gao, M., & Zhu, J. (2022) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2022.114824 · Cited by 31 · see citing works
Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening
Arcon, J. P., Defelipe, L. A., Lopez, E. D., Burastero, O., Modenutti, C. P., Barril, X., Marti, M. A., & Turjanski, A. G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00371 · Cited by 29 · see citing works
Ibrahim, T. M., Bauer, M. R., Dörr, A., Veyisoglu, E., & Boeckler, F. M. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00475 · Cited by 28 · see citing works
Uehara, S., & Tanaka, S. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00791 · Cited by 28 · see citing works
AutoPH4: An Automated Method for Generating Pharmacophore Models from Protein Binding Pockets
Jiang, S., Fehér, M., Williams, C., Cole, B., & Shaw, D. E. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00121 · Cited by 27 · see citing works
Predicting protein–ligand affinity with a random matrix framework
Lee, A. A., Brenner, M. P., & Colwell, L. J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611138113 · Cited by 27 · see citing works
Zhou, M., Ma, X., Sun, J., Ding, G., Cui, Q., Yan, M., Hou, Y., Jiang, M., & Bai, G. (2016) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-016-0043-6 · Cited by 25 · see citing works
Maximizing gain in high-throughput screening using conformal prediction
Svensson, F., Afzal, A. M., Norinder, U., & Bender, A. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0260-4 · Cited by 24 · see citing works
Jernigan, F. E., Hanai, J., Sukhatme, V. P., & Sun, L. (2017) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2017.01.001 · Cited by 22 · see citing works
Crisan, L., Istrate, D., Bora, A., & Pacureanu, L. M. (2020) · Molecular Diversity · doi:10.1007/s11030-020-10155-6 · Cited by 21 · see citing works
Binding mode information improves fragment docking
Jacquemard, C., Drwal, M. N., Desaphy, J., & Kellenberger, E. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0346-7 · Cited by 21 · see citing works
Pedretti, A., Mazzolari, A., Gervasoni, S. I., & Vistoli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20092060 · Cited by 22 · see citing works
Ibrahim, R. S., Mahrous, R. S., Fathy, H. M., Omar, A. A., & El-Khair, R. M. A. (2020) · Medicinal Chemistry Research · doi:10.1007/s00044-020-02516-5 · Cited by 20 · see citing works
OptiPharm: An evolutionary algorithm to compare shape similarity
Martín, S. P., Redondo, J. L., Ortigosa, P. M., & Pérez‐Sánchez, H. (2019) · Scientific Reports · doi:10.1038/s41598-018-37908-6 · Cited by 20 · see citing works
Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement
Tran‐Nguyen, V., & Rognan, D. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21124380 · Cited by 18 · see citing works
Modulators of the Human ABCC2: Hope from Natural Sources?
Comsa, E., Crișan, G., Loghin, F., & Falson, P. (2015) · Future Medicinal Chemistry · doi:10.4155/fmc.15.131 · Cited by 17 · see citing works
Prasetiyo, A., Kumala, S., Mumpuni, E., & Tjandrawinata, R. R. (2022) · Research Results in Pharmacology · doi:10.3897/rrpharmacology.8.76237 · Cited by 16 · see citing works
Yao, Y., Chen, F., Wu, C., Chang, X., Cheng, W., Wang, Q., Deng, Z., Liu, T., & Lü, L. (2025) · Plant Biotechnology Journal · doi:10.1111/pbi.70002 · Cited by 16 · see citing works
Nakatani, Y., Jiao, W., Aragão, D., Shimaki, Y., Petri, J., Parker, E. J., & Cook, G. M. (2017) · Acta Crystallographica Section F Structural Biology Communications · doi:10.1107/s2053230x17013073 · Cited by 16 · see citing works
Andrianov, G. V., Ong, W. J. G., Serebriiskii, I. G., & Karanicolas, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00630 · Cited by 15 · see citing works
Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study
Imbernón, B., Prades, J., Domingo Giménez, C., Cecilia, J. M., & Silla, F. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.050 · Cited by 14 · see citing works
Critical Assessment of State‐of‐the‐Art Ligand‐Based Virtual Screening Methods
Sciabola, S., Torella, R., Nagata, A., & Boehm, M. (2022) · Molecular Informatics · doi:10.1002/minf.202200103 · Cited by 13 · see citing works
Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)
Venkatraman, V., Gaiser, J., Demekas, D., Roy, A., Xiong, R., & Wheeler, T. J. (2024) · Pharmaceuticals · doi:10.3390/ph17080992 · Cited by 13 · see citing works
Panday, S. K., & Ghosh, I. (2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-05282-9_5 · Cited by 13 · see citing works
Xia, J., Li, S., Ding, Y., Wu, S., & Wang, X. S. (2019) · Molecular Informatics · doi:10.1002/minf.201900151 · Cited by 10 · see citing works
Computational Modeling of Multi-target-Directed Inhibitors Against Alzheimer’s Disease
Kumar, A., & Sharma, A. K. (2017) · Neuromethods · doi:10.1007/978-1-4939-7404-7_19 · Cited by 10 · see citing works
Shen, Z., Chen, R., Gao, J. F., Chi, X., Zhang, Q., Bian, Q., Zhou, B., Che, J., Dai, H., & Dong, X. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01818 · Cited by 9 · see citing works
Mavrogeni, M. E., Pronios, F., Zareifi, D., Vasilakaki, S., Lozach, O., Alexopoulos, L. G., Meijer, L., Myrianthopoulos, V., & Mikros, E. (2018) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0198 · Cited by 9 · see citing works
Bhavyashree, N., Vaishnavi, M. S., Shravani, P., & Sabat, S. (2024) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-024-01489-1 · Cited by 7 · see citing works
Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance
Pinel, P., Guichaoua, G., Najm, M., Labouille, S., Drizard, N., Gaston‐Mathé, Y., Hoffmann, B., & Stoven, V. (2023) · Molecular Informatics · doi:10.1002/minf.202200216 · Cited by 7 · see citing works
Developing Generalizable Scoring Functions for Molecular Docking: Challenges and Perspectives
Quiroga, R., & Villarreal, M. A. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673334469241017053508 · Cited by 7 · see citing works
In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies
Myrianthopoulos, V., Lambrinidis, G., & Mikros, E. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8630-9_15 · Cited by 7 · see citing works
Khan, M. F., Rashid, R. B., & Rashid, M. A. (2021) · Letters in Drug Design & Discovery · doi:10.2174/1570180818666210728162055 · Cited by 6 · see citing works
Fine tuning for success in structure-based virtual screening
Pihan, É., Kotev, M. I., Rabal, O., Beato, C., & Gonzalez, C. D. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-021-00431-4 · Cited by 6 · see citing works
Computational Modeling of Drugs for Alzheimer’s Disease: Design of Serotonin 5-HT6 Antagonists
Kelemen, Á. A., Mordalski, S., Bojarski, A. J., & Keserű, G. M. (2017) · Neuromethods · doi:10.1007/978-1-4939-7404-7_15 · Cited by 5 · see citing works
The use of dynamic computational models of neural circuitry to streamline new drug development
Arle, J. E., & Carlson, K. W. (2016) · Drug Discovery Today Disease Models · doi:10.1016/j.ddmod.2017.01.002 · Cited by 4 · see citing works
Chemoinformatics in Natural Product Research
Kaserer, T., Schuster, D., & Rollinger, J. M. (2018) · Wiley eBooks · doi:10.1002/9783527806539.ch6c · Cited by 4 · see citing works
Zetta, D. N., Shoombuatong, W., & Srisongkram, T. (2025) · ACS Omega · doi:10.1021/acsomega.5c04016 · Cited by 3 · see citing works
Deep reinforcement learning enables better bias control in benchmark for virtual screening
Shen, T., Li, S., Wang, X. S., Wang, D., Wu, S., Xia, J., & Zhang, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108165 · Cited by 3 · see citing works
Increasing Molecular Dynamics Simulations Throughput by Virtualizing Remote GPUs with rCUDA
Prades, J., Reaño, C., Silla, F., Imbernón, B., Pérez‐Sánchez, H., & Cecilia, J. M. (2018) · International Conference on Parallel Processing Workshops (ICPPW) · doi:10.1145/3229710.3229734 · Cited by 3 · see citing works
Olotu, F. A., Tali, M. B. T., Chepsiror, C., Amamuddy, O. S., Boyom, F. F., & Bishop, Ö. T. (2024) · International Journal for Parasitology Drugs and Drug Resistance · doi:10.1016/j.ijpddr.2024.100548 · Cited by 3 · see citing works
Virtual Libraries for Docking Methods: Guidelines for the Selection and the Preparation
Sellami, A., Réau, M. F., Langenfeld, F., Lagarde, N., & Montès, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00017-5 · Cited by 3 · see citing works
Self-citationArifuzzaman, Mohammadi, M., Rupa, F. H., Khan, M. F., Rashid, R. B., & Rashid, M. A. (2022) · Dhaka University Journal of Pharmaceutical Sciences · doi:10.3329/dujps.v21i1.60391 · Cited by 3 · see citing works
Pillanjinayya, S., Nagaraja, B., S, V. M., & Sabat, S. (2024) · Current Bioactive Compounds · doi:10.2174/0115734072309616240613071448 · Cited by 2 · see citing works
DUBS: A Framework for Developing D irectory of U seful B enchmarking S ets for Virtual Screening
Fine, J. A., Muhoberac, M., Fraux, G., & Chopra, G. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00122 · Cited by 2 · see citing works
Molecular docking and pharmacophore modelling; A bridged explanation with emphasis on validation
Mahrous, R. S., Fathy, H. M., El-Khair, R. M. A., Omar, A. A., & Ibrahim, R. S. (2024) · Journal of Advanced Pharmaceutical Sciences · doi:10.21608/japs.2024.253925.1007 · Cited by 1 · see citing works
Computational discovery of natural medicines targeting adenosine receptors for metabolic diseases
Wang, P., Xu, Z., Deng, Y., Yuan, H., Wu, Y.-K., Jiang, J., Lyu, Z., & Li, Z. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1671415 · Cited by 1 · see citing works
Representative Random Sampling of Chemical Space
Monterrubio-Chanca, D. J., & von Rudorff, G. F. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01523 · Cited by 1 · see citing works
Gupta, S., Waseem, M., Meena, N. K., Kuntal, R., Lynn, A. M., & Mishra, S. (2021) · Springer eBooks · doi:10.1007/978-981-15-8936-2_2 · Cited by 1 · see citing works
Maximizing resource usage in multifold molecular dynamics with rCUDA
Prades, J., Imbernón, B., Reaño, C., Peña-García, J. J., Cerón‐Carrasco, J. P., Silla, F., & Pérez‐Sánchez, H. (2019) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342019857131 · Cited by 1 · see citing works
Community Benchmarking Exercises for Docking and Scoring
Devi, B., Baidya, A. T. K., & Kumar, R. (2024) · doi:10.1002/9783527840748.ch20 · Cited by 1 · see citing works
Computational studies of biomolecular screening and interactions
Niinivehmas, S. P. (2015) · Jyväskylä University Digital Archive (University of Jyväskylä)
Using 3D Structural Information in Computational Design
Davis, A. M., & Edge, C. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.12339-x
Lindh, M. (2017) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Application of Molecular Modelling to Speed-up the Lead Discovery Process
Trist, I. M. L., Botta, M., & Fallacara, A. L. (2017) · Royal Society of Chemistry eBooks · doi:10.1039/9781788010139-00281
Bortolato, A., & Mason, J. S. (2022) · doi:10.1002/9781119627784.ch9
In Silico Approaches for Drug Discovery Focus on Virtual Screenings
Distinto, S., & Maccioni, E. (2022) · International Scientific Conference Sinteza · doi:10.15308/sinteza-2022-360-364
Empereur-mot, C. (2017) · theses.fr (ABES) · doi:10.70675/2b288725z2446z48c5z833ezf53915cd324b
Beyond Docking: Intelligent Modeling and the New Landscape of Drug-Protein Interactions
Taha, M. M., Tayel, A., & Shoeib, H. O. T. A. T. (2025) · Biomedical engineering · doi:10.5772/intechopen.1013595
- Authors
- Lagarde, N., Zagury, J.-F., & Montes, M.
- Affiliations
- Conservatoire National des Arts et Métiers (FR)
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003 Paris, France
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-06-18
- Volume
- 55
- Issue
- 7
- Pages
- 1297-1307
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2400913246
- Cited by
- 85
- References
- 93
- Citations by year
- 2025: 5 · 2024: 10 · 2023: 3 · 2022: 6 · 2021: 9 · 2020: 8 · 2019: 10 · 2018: 10 · 2017: 13 · 2016: 6 · 2015: 5
- FWCI
- 1.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Funders
- Conservatoire National des Arts et Métiers
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- virtual screening, method evaluation, enrichment, active compounds, decoys, protein structure, drug discovery, retrospective evaluation
- MeSH terms
- Drug Evaluation, Preclinical, Humans, Ligands, Proteins, User-Computer Interface, Benchmarking
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review