Reinforcing the Supply Chain of COVID-19 Therapeutics with Expert-Coded Retrosynthetic Software

Autor: Cernak Tim, mahjour babak, Brugger Nadia, McGrath Andrew, Jasty Shashi, Shim Eunjae, Lin Yingfu, wang i, Trice Sarah, zhang rui, Turnbull Rachel, Zhang Zirong, Shen Yuning
Rok vydání: 2020
Předmět:
DOI: 10.26434/chemrxiv.12765410.v1
Popis: 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.
Databáze: OpenAIRE