The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Review of Drug Repositioning Approaches and Resources
Open accessAbstract
Drug discovery is a time-consuming, high-investment, and high-risk process in traditional drug development. Drug repositioning has become a popular strategy in recent years. Different from traditional drug development strategies, the strategy is efficient, economical and riskless. There are usually three kinds of approaches: computational approaches, biological experimental approaches, and mixed approaches, all of which are widely used in drug repositioning. In this paper, we reviewed computational approaches and highlighted their characteristics to provide references for researchers to develop more powerful approaches. At the same time, the important findings obtained using these approaches are listed. Furthermore, we summarized 76 important resources about drug repositioning. Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment.
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Cited by
Drug repurposing for cancer therapy
Xia, Y., Sun, M., Huang, H., & Jin, W.-L. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01808-1 · Cited by 443
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“Multiple methods have been developed to achieve this goal, including computational methods, biological experimental methods, and the combination of both [5].”
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Drug–target interaction predication via multi-channel graph neural networks
Li, Y., Qiao, G., Wang, K., & Wang, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab346 · Cited by 112
Metadata
- Authors
- Xue, H., Li, J., Xie, H., & Wang, Y.
- Venue
- International Journal of Biological Sciences
- Publisher
- Ivyspring International Publisher
- Published
- 2018
- Volume
- 14
- Issue
- 10
- Pages
- 1232-1244
- ISSN
- 1449-2288 (ISSN-L)
- OpenAlex
- W2887766329
- Cited by
- 614
- References
- 93
- FWCI
- 44.38
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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