An artificial intelligence performing experiments using a synthesis robot has fished out what may be the most generic conditions for cross-coupling reactions from a pool of thousands of possible combinations. The AI-created reaction more than doubled the average yield in 20 tricky cross couplings compared with benchmark conditions.

Reaction conditions that work for compounds with different shapes, sizes and functional groups are ‘critical for automating small molecule synthesis, which in turn is critical for democratising molecular innovation’, says study leader Martin Burke from the University of Illinois Urbana–Champaign, US. In 2009, Burke’s team created a version of the Suzuki–Miyaura cross coupling that combines chloroarenes and N-methyliminodiacetic acid (Mida) boronates.

The team then turned to AI to adapt the reaction so it would work for the broadest possible substrate range. They asked it to scour the literature and find the most generic reaction conditions out of an almost infinite number of catalyst, ligand, temperature and base combinations. Yet rather than finding new, general conditions, all the algorithm would ‘discover’ were the conditions that are already the most popular. One of the reasons, Burke explains, is that ‘the literature is remarkably deficient in negative data. No one publishes what doesn’t work.’ But algorithms need both good and bad examples to learn from.