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
Items 501–518 of 518
Structure-Based Virtual Screening : New Methods and Applications in Infectious Diseases
Jacobsson, M. (2008) · Uppsala University Publications (Uppsala University)
A Novel Approach to Reduce High-Dimensional Search Spaces for the Molecular Docking Problem
Kuhn, D., Günther, R., & Weicker, K. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-540-89619-7_14
Empereur-mot, C. (2017) · theses.fr (ABES) · doi:10.70675/2b288725z2446z48c5z833ezf53915cd324b
Enhancing Drug Discovery and Development Through Artificial Intelligence
Espaillat, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-19336-0_12
Dilber, Y., Dilber, E., & Domaniç, K. Y. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48060630
Avula, B., Bhimireddi, R., Kumar, A., Pandey, S., Tungala, K., Eraganaboyina, S., Ravi, G., & Singh, A. K. (2026) · Journal of the Iranian Chemical Society · doi:10.1007/s13738-026-03481-3
Amjad, M., Waqas, M., Jamshed, A., Gormani, A. H., & Zafar, E. (2026) · In Silico Pharmacology · doi:10.1007/s40203-026-00693-6
In silico drug design and analysis of HGPRT inhibitor against malaria
Omar, N., & Gupta, S. (2026) · AIP conference proceedings · doi:10.1063/5.0287651
Biophysical analysis of therapeutic proteins: methods and applications
Amin, Z., Sunny, N., & Chugh, J. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44536-1.00007-9
BindingDB Entry 50019094: Structural Analysis of Chemokine Receptor-Ligand Interactions.
BindingDB (2024) · UC San Diego · doi:10.7270/q27m0d56
Sediri, K., Bennabi, L., Ayachi, H., Belarbi, L., Khane, Y., Bennabi, F., Dehbi, A., Messori, M., & Alsalme, A. (2026) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-026-06035-9
Theoretical and experimental studies of thermolysin inhibition
Wuxiuer, Y. (2008) · NORA - Norwegian Open Research Archives
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
