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• W3204114718 startingPage "770" @default.
• W3204114718 abstract "Discovery and development of novel pharmaceuticals continue to be a very costly, time-consuming and uncertain process impacting negatively not only the research and development of pharmaceutical industry but also health care. Although the number of novel drugs approved each year has grown over by as much as 60% compared to the past decade, there are still many diseases that do not have any approved drug. Recent technological advances in the biomedical, genomics, and computational science domains accompanied by multisource and multidimensional data opened new opportunities and challenges. The drug discovery paradigm is increasingly shifting from hypothesis-driven to data-driven approaches. While the search for the magic bullets of medicine continues, the magic—crunching the data deluge into knowledge and hypotheses nuggets—is mostly driven by machines and machine intelligence. This review will primarily focus on three facets of computational drug discovery approaches, namely, drug repositioning, de novo drug discovery, and drug combinations, and reflect on computational approaches which are reproducible and seem most promising for the machine learning-driven drug discovery. Finally, using COVID-19 as an example, we discuss how the computational approaches are aiding and accelerating the process of discovery of magic bullet(s) for this dreadful pandemic." @default.
• W3204114718 created "2021-10-11" @default.
• W3204114718 creator A5023311059 @default.
• W3204114718 creator A5061689835 @default.
• W3204114718 date "2022-01-01" @default.
• W3204114718 modified "2023-09-25" @default.
• W3204114718 title "Magic bullets: Drug repositioning and drug combinations" @default.
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