The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Drug repositioning by structure-based virtual screening
Abstract
Approved drugs have favourable or validated pharmacokinetic properties and toxicological profiles, and the repositioning of existing drugs for new indications can potentially avoid expensive costs associated with early-stage testing of the hit compounds. In recent years, technological advances in virtual screening methodologies have allowed medicinal chemists to rapidly screen drug libraries for therapeutic activity against new biomolecular targets in a cost-effective manner. This review article outlines the basic principles and recent advances in structure-based virtual screening and highlights the powerful synergy of in silico techniques in drug repositioning as demonstrated in several recent reports.
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.
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
DrugBank: a comprehensive resource for in silico drug discovery and exploration
Wishart, D. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj067 · Cited by 3,892
Drug repositioning: identifying and developing new uses for existing drugs
Ashburn, T. T., & Thor, K. B. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1468 · Cited by 3,237
The structure and function of G-protein-coupled receptors
Rosenbaum, D. M., Rasmussen, S. G. F., & Kobilka, B. K. (2009) · Nature · doi:10.1038/nature08144 · Cited by 2,534
KEGG for representation and analysis of molecular networks involving diseases and drugs
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322
Predicting new molecular targets for known drugs
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667
Hot spots—A review of the protein–protein interface determinant amino‐acid residues
Moreira, I. S., Fernandes, P. A., & Ramos, M. J. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21396 · Cited by 760
Digoxin and other cardiac glycosides inhibit HIF-1α synthesis and block tumor growth
Zhang, H., Qian, D. Z., Tan, Y. S., Lee, K., Gao, P., Ren, Y. R., Rey, S., Hammers, H., Chang, D., Pili, R., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0809763105 · Cited by 624
Flexible ligand docking to multiple receptor conformations: a practical alternative
Totrov, M., & Abagyan, R. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.01.004 · Cited by 497
Lin, X., Huang, X.-P., Chen, G., Whaley, R., Peng, S., Wang, Y., Zhang, G., Wang, S. X., Wang, S., Roth, B. L., et al. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300338m · Cited by 73
Wei, D., Zheng, H., Su, N., Deng, M., & Lai, L. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900463u · Cited by 22
Cited by
Review of Drug Repositioning Approaches and Resources
Xue, H., Li, J., Xie, H., & Wang, Y. (2018) · International Journal of Biological Sciences · doi:10.7150/ijbs.24612 · Cited by 614
An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
Li, J., Fu, A., & Zhang, L. (2019) · Interdisciplinary Sciences: Computational Life Sciences · doi:10.1007/s12539-019-00327-w · Cited by 434
Recent development of biotin conjugation in biological imaging, sensing, and target delivery
Ren, W. X., Han, J., Uhm, S., Jang, Y. J., Kang, C., Kim, J.-H., & Kim, J. S. (2015) · Chemical Communications · doi:10.1039/c5cc03075g · Cited by 393
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
Ain, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J. (2015) · WIREs Computational Molecular Science · doi:10.1002/wcms.1225 · Cited by 357
Metadata
- Authors
- Ma, D.-L., Chan, D. S.-H., & Leung, C.-H.
- Venue
- Chemical Society Reviews
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2013
- Volume
- 42
- Issue
- 5
- Pages
- 2130
- ISSN
- 0306-0012 (ISSN-L)
- 1460-4744
- OpenAlex
- W2020639753
- Cited by
- 232
- References
- 11
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Drug repositioning by structure-based virtual screening. https://app.cochat.ai/papers/10.1039/c2cs35357a
- The paper
- Ma, D.-L., Chan, D. S.-H., & Leung, C.-H. (2013). Drug repositioning by structure-based virtual screening. Chemical Society Reviews. https://doi.org/10.1039/c2cs35357a
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
