Drug repurposing: progress, challenges and recommendations

By S. Pushpakom, Francesco Iorio, P. Eyers, K. J. Escott, Shirley Hopper, A. Wells, Andrew J. Doig, Tim Guilliams, J. Latimer, Christina McNamee and 4 more. Nature reviews. Drug discovery, 2018

3,866 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Infectious Diseases, Pharmacology

View Paper: https://www.semanticscholar.org/paper/1faa34eb2e62f8953ec8c29c20626165f30df331

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In Computational Theory and Mathematics

In Pharmacology

Citations in the corpus

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “Getting a drug to market currently takes 13-15 years and between US$2 billion and $3 billion on average, and the costs are going up (Pushpakom et al. 2019).”
Axes of a revolution: challenges and promises of big data in healthcare (2020, background): “Systematic analysis of large-scale data by various computational approaches can also be used to obtain meaningful interpretations for the repurposing of existing drugs 109 .”
The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets (2020, background): “They also support applications such as drug target discovery or drug repurposing (11,12), and can help in the interpretation of genomic variation (13).”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “Recently, drug repurposing has emerged as an innovative approach to minimize drug development duration that usually involves data mining and AI [438].”