Artificial intelligence finds alternative routes to COVID-19 drug candidates

Drug-repurposing studies are testing a range of compounds to treat COVID-19, but manufacturers may struggle to meet demand if any of these candidates prove effective against SARS-CoV-2. The pandemic has already strained global supply chains and limited the availability of a number of products, including hand sanitizer and diagnostic test reagents. The raw materials needed to make a new antiviral drug would most likely face similar pressures. But a team led by Tim Cernak of the University of Michigan has used an AI-based retrosynthesis program called Synthia to devise alternative routes to 12 leading drug candidates under investigation. The work appears on a preprint server and has not been peer reviewed (ChemRxiv 2020, DOI: 10.26434/chemrxiv.12765410.v1). “If the world runs out of one of the drugs currently in the clinic, we are providing a backup recipe,” Cernak says. Using alternative starting materials that are readily available, the researchers aimed to find routes of similar length and cost to those of existing syntheses. For each compound, the researchers whittled down a long list of options offered by Synthia to identify the most suitable synthetic strategies. Then the team tested some of these syntheses in the lab, including four new routes to the antiviral umifenovir, currently being investigated in eight clinical trials against COVID-19. Cernak says this approach could be used more generally to rapidly identify alternative synthetic routes whenever crises cause supply chain disruptions in drug manufacturing.