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SYNTHIA COVID Cernak Lab Paper 08-05-20.pdf (4.91 MB)
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Reinforcing the Supply Chain of COVID-19 Therapeutics with Expert-Coded Retrosynthetic Software

preprint
submitted on 05.08.2020 and posted on 07.08.2020 by Yingfu Lin, Zirong Zhang, Babak Mahjour, Di Wang, Rui Zhang, Eunjae Shim, Andrew McGrath, Yuning Shen, Nadia Brugger, Rachel Turnbull, Shashi Jasty, Sarah Trice, Tim Cernak
Supply chains become stressed when demand for essential products increases rapidly in times of crisis. This year, the scourge of coronavirus highlighted the fragility of diverse supply chains, affecting the world’s pipeline of hand sanitizer, 1 toilet paper,2 and pharmaceutical starting materials. 3 Many drug repurposing studies are now underway. 4 If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it,5 will be available in the quantities required to satisfy global demand. We show the use of a retrosynthetic artificial intelligence (AI) 6-10 to navigate multiple parallel synthetic sequences, and arrive at plausible alternate reagent supply chains for twelve investigational COVID-19 therapeutics. In many instances, the AI utilizes C–H functionalization logic, 11-13 and we have experimentally validated several syntheses, including a route to the antiviral umifenovir that requires functionalization of six C–H bonds. This general solution to chemical supply chain reinforcement will be useful during global disruptions, such as during a pandemic.

History

Email Address of Submitting Author

tcernak@med.umich.edu

Institution

University of Michigan

Country

USA

ORCID For Submitting Author

0000-0001-5407-0643

Declaration of Conflict of Interest

The Regents of the University of Michigan have patented this work. This work was funded by MilliporeSigma.

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