Predicting Reaction Yields via Supervised Learning
Published in Accounts of Chemical Research, 2021
Recommended citation: Zuranski, Andrzej M.; Martinez Alvarado, Jesus; Shields, Benjamin J.; Doyle, Abigail G. "Predicting Reaction Yields via Supervised Learning", Acc. Chem. Res., 2021, 54, 1856–1865. https://pubs.acs.org/doi/10.1021/acs.accounts.0c00770?ref=pdf
Statistical methods in chemistry have a rich history, but only recently has ML gained widespread attention in reaction development. As the untapped potential of ML is explored, novel tools are likely to arise from future research. Our studies suggest that supervised ML can lead to improved predictions of reaction yield over simpler modeling methods and facilitate mechanistic understanding of reaction dynamics. However, further research and development is required to establish ML as an indispensable tool in reactivity modeling.