Machine learning triumphs in tough cross coupling challenge

The yields of tricky cross coupling reactions can now be accurately predicted by a computer program that taught itself how to tackle this tough problem. Key to the algorithm's expertise is the data it trained on from thousands of small scale reactions. 'The big goal, which this is a small step toward, is to be able to predict reaction performance of new substrates without experimentation,' explains Abigail Doyle from Princeton University, who led the work together with Spencer Dreher from Merck & Co. Machine learning has helped scientists explore chemical space, find new synthetic pathways and predict reaction outcomes. However, yield prediction software still often gets things wrong. This is because the data algorithms have to work with – reaction parameters collected by many groups over the years – is often inconsistent and incomplete. Reactions that don't work, for example, are usually not reported.