This paper describes a Machine Learning model that aims to improve such catalyst-reaction assignments based on the carefully curated literature data. As we show here for the case of asymmetric magnesium catalysis, this model achieves relatively high accuracy and offers out of-the-box predictions successfully validated by experiment, e.g., in synthetically demanding asymmetric reductions or Michael additions.
P. Baczewska, M. Kulczykowski, B. Zambroń, J. Jaszczewska-Adamczak, Z. Pakulski, R. Roszak, B. A. Grzybowski, J. Mlynarski “Machine Learning Algorithm Guides Catalyst Choices for Magnesium-Catalyzed Asymmetric Reactions” Angew. Chem. Int. Ed.