Machine learning model turns decades of literature data into a fast route to highly selective organocatalysis
A Tsinghua University team built OrgAIcat, a machine learning framework trained on more than 22,000 literature data points that predicts enantioselectivity in organocatalytic reactions and, combined with Bayesian optimization, raised a challenging Aldol reaction from 41% to 96% ee in only 12 experiments.