期刊
PHARMACEUTICALS
卷 11, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/ph11020057
关键词
computational drug repositioning; drug repositioning; drug repurposing; machine learning; deep learning; crowdsourcing; open innovation; drug discovery
资金
- NIH NHLBI [1R21 HL133539, 1R21 HL135368]
Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocatedThe most fruitful basis for the discovery of a new drug is to start with an old drug. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of serendipity, discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodulespharmacologically relevant sub-networks of biomolecules and/or pathwaysfrom collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.
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