4.6 Review

Computational Drug Repositioning: Current Progress and Challenges

期刊

APPLIED SCIENCES-BASEL
卷 10, 期 15, 页码 -

出版社

MDPI
DOI: 10.3390/app10155076

关键词

drug repositioning; drug discovery; machine learning; pharmacogenetics

资金

  1. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2020R1F1A1069672]
  2. Hankuk University of Foreign Studies Research Fund
  3. National Research Foundation of Korea [2020R1F1A1069672] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据