Affiliation:
1. Institute of Organic Chemistry Polish Academy of Sciences Kasprzaka 44/52 02-224 Warsaw Poland
2. Center for Algorithmic and Robotized Synthesis (CARS) of Korea's Institute for Basic Science (IBS) and Department of Chemistry Ulsan National Institute of Science and Technology 50 UNIST-gil Eonyang-eup Ulju-gun Ulsan 44919 South Korea
Abstract
AbstractOrganic‐chemical literature encompasses large numbers of catalysts and reactions they can effect. Many of these examples are published merely to document the catalysts’ scope but do not necessarily guarantee that a given catalyst is “optimal”—in terms of yield or enantiomeric excess—for a particular reaction. 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.