Review of Drug Repositioning Approaches and Resources
By Hanqing Xue, Jie Li, Haozhe Xie, Yadong Wang. International Journal on Biological Sciences, 2018
497 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/98214486d16c6f2fa9e018a652fff8f7ddb43969
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In Computational Theory and Mathematics
In Molecular Biology
Citations in the corpus
- Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19 (2020, background): “In such cases, reusing drugs for more than one purpose and finding their new uses can significantly reduce the cost, time and risks of the drug development process using fast-growing computational approaches (Xue et al., 2018).”
- Machine learning applications in drug development (2019, background): “This pipeline takes at least 5 years to be completed [1], and can last up to 15 years [2].”