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Pharmaceuticals 2018, 11(2), 57;

Changing Trends in Computational Drug Repositioning

Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH 45219, USA
Author to whom correspondence should be addressed.
Received: 30 April 2018 / Revised: 1 June 2018 / Accepted: 2 June 2018 / Published: 5 June 2018
(This article belongs to the Special Issue Old Pharmaceuticals with New Applications)
PDF [288 KB, uploaded 5 June 2018]


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 advocated—“The 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 pharmacomodules—pharmacologically relevant sub-networks of biomolecules and/or pathways—from 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. View Full-Text
Keywords: computational drug repositioning; drug repositioning; drug repurposing; machine learning; deep learning; crowdsourcing; open innovation; drug discovery computational drug repositioning; drug repositioning; drug repurposing; machine learning; deep learning; crowdsourcing; open innovation; drug discovery
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Yella, J.K.; Yaddanapudi, S.; Wang, Y.; Jegga, A.G. Changing Trends in Computational Drug Repositioning. Pharmaceuticals 2018, 11, 57.

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