The DOI matches its Crossref record; 204 of the 209 references it cites were found in a registry or an index.
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go
Open accessAbstract
Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“The CS strategy could overcome to some extent the limitations faced by the single-scoring approach, for example, the inconsistent performances across different protein targets and chemical classes (Moitessier et al., 2009).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Moitessier et al., 2009
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051256o · Cited by 7,012
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
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 4,354
HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information
Dominguez, C., Boelens, R., & Bonvin, A. M. J. J. (2003) · Journal of the American Chemical Society · doi:10.1021/ja026939x · Cited by 3,187
Improved protein–ligand docking using GOLD
Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W., & Taylor, R. D. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10465 · Cited by 3,139
PatchDock and SymmDock: servers for rigid and symmetric docking
Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki481 · Cited by 3,018
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
MolDock: A New Technique for High-Accuracy Molecular Docking
Thomsen, R., & Christensen, M. H. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051197e · Cited by 2,381
Novel Procedure for Modeling Ligand/Receptor Induced Fit Effects
Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A., & Farid, R. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050540c · Cited by 2,089
Flagged first, then by citations
5 of the 209 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Korb O, 2006, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 247
- Shoichet BK, 2002, Hits, leads and artifacts from virtual and high throughput screening, Molecular Informatics: Confronting Complexity May 13th–16th 2002
- 10.1002/(SICI)1097-0134(1997)1 <215::AID-PROT29>3.0.CO;2-Q · DOI
- 10.1002/(SICI)1096-987X(19981115)19:14<1623::AID-JCC8>3.0.CO;2-L · DOI
- 10.1002/(SICI)1096-987X(19981115)19:14<1612::AID-JCC7>3.0.CO;2-M · DOI
Cited by
Sorted by citations
Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery
Meng, X.-Y., Zhang, H.-X., Mezei, M., & Cui, M. (2011) · Current Computer Aided-Drug Design · doi:10.2174/157340911795677602 · Cited by 3,771
Molecular Docking and Structure-Based Drug Design Strategies
Ferreira, L. L. G., dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015) · Molecules · doi:10.3390/molecules200713384 · Cited by 2,722
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
Ballester, P. J., & Mitchell, J. B. O. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq112 · Cited by 1,015
Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp01555g · Cited by 946
A Structure-Based Drug Discovery Paradigm
Batool, M., Ahmad, S. B., & Choi, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112783 · Cited by 785
Virtual Screening Strategies in Drug Discovery: A Critical Review
Lavecchia, A., & Giovanni, C. D. (2013) · Current Medicinal Chemistry · doi:10.2174/09298673113209990001 · Cited by 712
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 703
Receptor–ligand molecular docking
Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2013) · Biophysical Reviews · doi:10.1007/s12551-013-0130-2 · Cited by 668
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
Cheng, T., Li, Q., Zhou, Z., Wang, Y., & Bryant, S. H. (2012) · The AAPS Journal · doi:10.1208/s12248-012-9322-0 · Cited by 587
Experimental Binding Energies in Supramolecular Complexes
Biedermann, F., & Schneider, H. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00583 · Cited by 575
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
Variability in docking success rates due to dataset preparation.
Corbeil, C. R., Williams, C. I., & Labute, P. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9570-1 · Cited by 469
Self-citationCan we trust docking results? Evaluation of seven commonly used programs on PDBbind database
Plewczyński, D., Łaźniewski, M., Augustyniak, R., & Ginalski, K. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21643 · Cited by 423
Docking and scoring with ICM: the benchmarking results and strategies for improvement
Neves, M. A. C., Totrov, M. M., & Abagyan, R. A. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9547-0 · Cited by 375
Chemical space as a source for new drugs
Reymond, J., van Deursen, R., Blum, L. C., & Ruddigkeit, L. (2010) · MedChemComm · doi:10.1039/c0md00020e · Cited by 359
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
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 325
Covalent inhibitors design and discovery.
De Cesco, S., Kurian, J., Dufresne, C., Mittermaier, A. K., & Moitessier, N. (2017) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2017.06.019 · Cited by 315
Self-citationSchneider, N., Lange, G., Hindle, S. A., Klein, R., & Rarey, M. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9626-2 · Cited by 320
Tallei, T. E., Tumilaar, S. G., Niode, N. J., Fatimawali, F., Kepel, B. J., Idroes, R., Effendi, Y., Sakib, S. A., & Emran, T. B. (2020) · Scientifica · doi:10.1155/2020/6307457 · Cited by 309
Rescifina, A., Zagni, C., Varrica, M. G., Pistarà, V., & Corsaro, A. (2014) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2013.11.029 · Cited by 276
Use of machine learning approaches for novel drug discovery
Lima, A. N., Philot, E. A., Trossini, G. H. G., Scott, A. L., Maltarollo, V. G., & Honório, K. M. (2016) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2016.1146250 · Cited by 260
Covalent docking using autodock: two-point attractor and flexible side chain methods.
Bianco, G., Forli, S., Goodsell, D. S., & Olson, A. J. (2016) · Protein Science · doi:10.1002/pro.2733 · Cited by 246
Managing protein flexibility in docking and its applications
B‐Rao, C., Subramanian, J., & Sharma, S. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.01.003 · Cited by 239
Molecular Docking of Intercalators and Groove-Binders to Nucleic Acids Using Autodock and Surflex
Holt, P. A., Chaires, J. B., & Trent, J. O. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800063v · Cited by 226
Stroganov, O. V., Novikov, F. N., Stroylov, V. S., Kulkov, V., & Chilov, G. G. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800166p · Cited by 228
Ricci, C. G., & Netz, P. A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9001537 · Cited by 217
Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics
Maximova, T., Moffatt, R., Ma, B., Nussinov, R., & Shehu, A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004619 · Cited by 214
Kufareva, I., & Abagyan, R. A. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8010299 · Cited by 212
Diazepam-bound GABAA receptor models identify new benzodiazepine binding-site ligands
Richter, L., de Graaf, C., Sieghart, W., Varagić, Z., Mörzinger, M., de Esch, I. J. P., Ecker, G. F., & Ernst, M. C. (2012) · Nature Chemical Biology · doi:10.1038/nchembio.917 · Cited by 200
Ballester, P. J., Schreyer, A. M., & Blundell, T. L. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500091r · Cited by 199
Bottegoni, G., Kufareva, I., Totrov, M. M., & Abagyan, R. A. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8009958 · Cited by 179
Software and Resources for Computational Medicinal Chemistry
Liao, C., Sitzmann, M., Pugliese, A., & Nicklaus, M. C. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.63 · Cited by 182
Zilian, D., & Sotriffer, C. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400120b · Cited by 176
PLATINUM: a web tool for analysis of hydrophobic/hydrophilic organization of biomolecular complexes
Pyrkov, T. V., Chugunov, A. O., Krylov, N. A., Nolde, D. E., & Efremov, R. G. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp111 · Cited by 174
Pattar, S. V., Adhoni, S. A., Kamanavalli, C. M., & Kumbar, S. S. (2020) · Beni-Suef University Journal of Basic and Applied Sciences · doi:10.1186/s43088-020-00059-7 · Cited by 170
Du, J., Sun, H., Xi, L., Li, J., Ying, Y., Liu, H., & Yao, X. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21859 · Cited by 171
Protein flexibility in docking and surface mapping
Lexa, K. W., & Carlson, H. A. (2012) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583512000066 · Cited by 177
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
Docking Studies on DNA Intercalators
Gilad, Y., & Senderowitz, H. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400352t · Cited by 153
Honarparvar, B., Govender, T., Maguire, G. E. M., Soliman, M. E. S., & Kruger, H. G. (2013) · Chemical Reviews · doi:10.1021/cr300314q · Cited by 163
Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction
Wan, S., Bhati, A. P., Zasada, S. J., & Coveney, P. V. (2020) · Interface Focus · doi:10.1098/rsfs.2020.0007 · Cited by 158
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 154
Structure‐Based Approaches to Target Fishing and Ligand Profiling
Rognan, D. (2010) · Molecular Informatics · doi:10.1002/minf.200900081 · Cited by 154
Concepts and applications of chemical fingerprint for hit and lead screening
Yang, J., Cai, Y., Zhao, K., Xie, H., & Chen, X. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103356 · Cited by 146
Damm-Ganamet, K. L., Smith, R. D., Dunbar, J. B., Stuckey, J. A., & Carlson, H. A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400025f · Cited by 133
Singh, T., Biswas, D., & Jayaram, B. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200193z · Cited by 133
idock: A multithreaded virtual screening tool for flexible ligand docking
Li, H., Leung, K., & Wong, M. (2012) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2012.6217214 · Cited by 128
istar: A Web Platform for Large-Scale Protein-Ligand Docking
Li, H., Leung, K., Ballester, P. J., & Wong, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085678 · Cited by 123
Structural Analysis of Chemokine Receptor–Ligand Interactions
Arimont, M., Sun, S., Leurs, R., Smit, M. J., de Esch, I. J. P., & de Graaf, C. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b01309 · Cited by 126
In silico approaches and tools for the prediction of drug metabolism and fate: A review
Kazmi, S. R., Ren, J., Yu, M., Jung, C., & Na, D. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.01.008 · Cited by 128
Spyrakis, F., & Cavasotto, C. N. (2015) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2015.08.002 · Cited by 124
Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening
Sato, T., Honma, T., & Yokoyama, S. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900382e · Cited by 123
Combined strategies in structure-based virtual screening
Wang, Z., Sun, H., Shen, C., Hu, X., Gao, J., Li, D., Cao, D., & Hou, T. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp06303j · Cited by 120
Outstanding challenges in protein–ligand docking and structure‐based virtual screening
Waszkowycz, B., Clark, D. E., & Gancia, E. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.18 · Cited by 117
Improved Ligand-Protein Binding Affinity Predictions Using Multiple Binding Modes
Stjernschantz, E., & Oostenbrink, C. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.02.034 · Cited by 119
Shannon information entropy in heavy-ion collisions
Ma, C.-W., & G., Y. (2018) · Progress in Particle and Nuclear Physics · doi:10.1016/j.ppnp.2018.01.002 · Cited by 113
Lemkul, J. A., & Bevan, D. R. (2012) · ACS Chemical Neuroscience · doi:10.1021/cn300091a · Cited by 109
Sanders, M. P. A., McGuire, R., Roumen, L., de Esch, I. J. P., de Vlieg, J., Klomp, J. P. G., & de Graaf, C. (2011) · MedChemComm · doi:10.1039/c1md00210d · Cited by 114
Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107
Corbeil, C. R., & Moitessier, N. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8004176 · Cited by 111
Self-citationBarillari, C., Marcou, G., & Rognan, D. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800064z · Cited by 111
Structure-Based Virtual Screening
Li, Q., & Shah, S. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6783-4_5 · Cited by 107
Tufféry, P., & Derreumaux, P. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0584 · Cited by 109
Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists
Koes, D. R., Khoury, K., Huang, Y., Wang, W., Biśta, M., Popowicz, G. M., Wolf, S., Holak, T. A., Dömling, A. S. S., & Camacho, C. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032839 · Cited by 105
VoteDock: Consensus docking method for prediction of protein–ligand interactions
Plewczyński, D., Łaźniewski, M., von Grotthuss, M., Rychlewski, L., & Ginalski, K. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21642 · Cited by 105
The impact of in silico screening in the discovery of novel and safer drug candidates
Rognan, D. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.02.034 · Cited by 105
Systematic exploitation of multiple receptor conformations for virtual ligand screening.
Bottegoni, G., Rocchia, W., Rueda, M., Abagyan, R., & Cavalli, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018845 · Cited by 96
Challenges and current status of computational methods for docking small molecules to nucleic acids
Luo, J., Wei, W., Waldispühl, J., & Moitessier, N. (2019) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2019.02.046 · Cited by 96
Self-citationVogel, S., Bauer, M. R., & Boeckler, F. M. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2001549 · Cited by 97
Mascarello, A., Chiaradia, L. D., Vernal, J. I., Villarino, A., Guido, R. V. C., Perizzolo, P. F., Poirier, V., Wong, D., Martins, P. G. A., Nunes, R. J., et al. (2010) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2010.04.051 · Cited by 94
pKa based protonation states and microspecies for protein–ligand docking
ten Brink, T., & Exner, T. E. (2010) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-010-9385-x · Cited by 99
“Selective” serotonin 5-HT2A receptor antagonists
Casey, A. B., Cui, M., Booth, R. G., & Canal, C. E. (2022) · Biochemical Pharmacology · doi:10.1016/j.bcp.2022.115028 · Cited by 94
Integrative Modelling of Biomolecular Complexes
Koukos, P. I., & Bonvin, A. M. J. J. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.11.009 · Cited by 94
Biomolecular simulation and modelling: status, progress and prospects
van der Kamp, M. W., Shaw, K. E., Woods, C. J., & Mulholland, A. J. (2008) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0105.focus · Cited by 90
Ellingson, S. R., Smith, J. C., & Baudry, J. Y. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23367 · Cited by 92
Sable, R. V., & Jois, S. D. S. (2015) · Molecules · doi:10.3390/molecules200611569 · Cited by 93
The compromise of virtual screening and its impact on drug discovery
Slater, O., & Kontoyianni, M. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1604677 · Cited by 91
Zhang, X., Wong, S., & Lightstone, F. C. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci4005145 · Cited by 91
Bioinformatics and variability in drug response: a protein structural perspective
Lahti, J. L., Tang, G. W., Capriotti, E., Liu, T., & Altman, R. B. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0843 · Cited by 89
Lagarde, N., Zagury, J.-F., & Montes, M. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00090 · Cited by 85
Advances and applications of binding affinity prediction methods in drug discovery
Parenti, M. D., & Rastelli, G. (2011) · Biotechnology Advances · doi:10.1016/j.biotechadv.2011.08.003 · Cited by 90
Englebienne, P., & Moitessier, N. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8004308 · Cited by 85
Self-citationMultipose Binding in Molecular Docking
Atkovska, K., Samsonov, S. A., Paszkowski‐Rogacz, M., & Pisabarro, M. T. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15022622 · Cited by 84
Charting a Path to Success in Virtual Screening
Forli, S. (2015) · Molecules · doi:10.3390/molecules201018732 · Cited by 81
New Insights Into DNA Helicases as Druggable Targets for Cancer Therapy
Datta, A., & Brosh, R. M. (2018) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2018.00059 · Cited by 83
Medicinal Chemistry Projects Requiring Imaginative Structure-Based Drug Design Methods
Moitessier, N., Pottel, J., Therrien, É., Englebienne, P., Liu, Z., Tomberg, A., & Corbeil, C. R. (2016) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00185 · Cited by 78
Self-citationTargeted scoring functions for virtual screening.
Seifert, M. H. J. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.03.013 · Cited by 76
Lithocholic acid is an endogenous inhibitor of MDM4 and MDM2
Vogel, S., Bauer, M. R., Joerger, A. C., Wilcken, R., Brandt, T., Veprintsev, D. B., Rutherford, T. J., Fersht, A. R., & Boeckler, F. M. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215060109 · Cited by 74
Villoutreix, B. O., Lagorce, D., Labbé, C. M., Spérandio, O., & Miteva, M. A. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.06.013 · Cited by 75
Development of covalent inhibitors: Principle, design, and application in cancer
Zheng, L., Li, Y., Wu, D., Xiao, H., Zheng, S., Wang, G., & Sun, Q. (2023) · MedComm – Oncology · doi:10.1002/mog2.56 · Cited by 71
Accounting for Induced-Fit Effects in Docking: What is Possible and What is Not?
Sotriffer, C. (2011) · Current Topics in Medicinal Chemistry · doi:10.2174/156802611794863544 · Cited by 76
Space filling of β-cyclodextrin and β-cyclodextrin derivatives by volatile hydrophobic guests
Fourmentin, S., Ciobanu, A., Landy, D., & Wenz, G. (2013) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.9.133 · Cited by 71
Zev, S., Raz, K., Schwartz, R., Tarabeh, R., Gupta, P. K., & Major, D. T. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00263 · Cited by 70
Role of Water in Molecular Docking Simulations of Cytochrome P450 2D6
Santos, R., Hritz, J., & Oostenbrink, C. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900293e · Cited by 71
Protein–Ligand Empirical Interaction Components for Virtual Screening
Yan, Y., Wang, W., Sun, Z., Zhang, J. Z. H., & Ji, C. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00017 · Cited by 69
Maffucci, I., Hu, X., Fumagalli, V., & Contini, A. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00043 · Cited by 73
Isolation, Purification and Molecular Mechanism of a Peanut Protein-Derived ACE-Inhibitory Peptide
Shi, A., Liu, H., Liu, L., Hu, H., Wang, Q., & Adhikari, B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111188 · Cited by 71
Customizing G Protein-Coupled Receptor Models for Structure-Based Virtual Screening
de Graaf, C., & Rognan, D. (2009) · Current Pharmaceutical Design · doi:10.2174/138161209789824786 · Cited by 67
Metadata
- Authors
- Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R.
- Affiliations
- McGill University (CA)
- Corresponding author
- Moitessier, N
- Venue
- British Journal of Pharmacology
- Publisher
- Wiley
- Published
- 2008-03
- Volume
- 153
- Issue
- S1
- Pages
- S7-26
- ISSN
- 0007-1188 (ISSN-L)
- 1476-5381
- PMC
- PMC2268060
- OpenAlex
- W2135817448
- Cited by
- 514
- References
- 209
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 14 · 2023: 19 · 2022: 24 · 2021: 24 · 2020: 16 · 2019: 18 · 2018: 18 · 2017: 26 · 2016: 30 · 2015: 38 · 2014: 34 · 2013: 37 · 2012: 43
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- British Journal of Pharmacology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pharmacology (medical) › Cholinesterase and Neurodegenerative Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- molecular docking, scoring functions, virtual screening, drug discovery, binding affinity prediction, therapeutic target
- MeSH terms
- Algorithms, Animals, Artificial Intelligence, Computer Simulation, Drug Evaluation, Preclinical, Humans, Metals, Models, Molecular, Molecular Conformation, Nucleic Acids, Proteins, Stochastic Processes, Reproducibility of Results
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