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迈向更好的药物重新定位:将现有方法进行优先级排序并整合到高效流程中。

Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines.

作者信息

Jin Guangxu, Wong Stephen T C

机构信息

Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA; NCI Center for Modeling Cancer Development, Houston Methodist Research Institute, Houston, TX 77030, USA.

Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA; NCI Center for Modeling Cancer Development, Houston Methodist Research Institute, Houston, TX 77030, USA.

出版信息

Drug Discov Today. 2014 May;19(5):637-44. doi: 10.1016/j.drudis.2013.11.005. Epub 2013 Nov 14.

DOI:10.1016/j.drudis.2013.11.005
PMID:24239728
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4021005/
Abstract

Recycling old drugs, rescuing shelved drugs and extending patents' lives make drug repositioning an attractive form of drug discovery. Drug repositioning accounts for approximately 30% of the newly US Food and Drug Administration (FDA)-approved drugs and vaccines in recent years. The prevalence of drug-repositioning studies has resulted in a variety of innovative computational methods for the identification of new opportunities for the use of old drugs. Questions often arise from customizing or optimizing these methods into efficient drug-repositioning pipelines for alternative applications. It requires a comprehensive understanding of the available methods gained by evaluating both biological and pharmaceutical knowledge and the elucidated mechanism-of-action of drugs. Here, we provide guidance for prioritizing and integrating drug-repositioning methods for specific drug-repositioning pipelines.

摘要

回收旧药、拯救搁置药物以及延长专利期限使药物重新定位成为一种颇具吸引力的药物研发形式。近年来,在美国食品药品监督管理局(FDA)新批准的药物和疫苗中,约30%来自药物重新定位。药物重新定位研究的盛行催生了多种创新的计算方法,用于识别旧药新的应用机会。将这些方法定制或优化为适用于其他应用的高效药物重新定位流程时,常常会出现问题。这需要通过评估生物学和药学知识以及已阐明的药物作用机制,全面了解现有方法。在此,我们为特定药物重新定位流程中药物重新定位方法的优先级排序和整合提供指导。

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Self-supervision advances morphological profiling by unlocking powerful image representations.自监督学习通过解锁强大的图像表示来推进形态学分析。
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