Synfacts 2019; 15(04): 0419
DOI: 10.1055/s-0037-1612338
Organo- and Biocatalysis
© Georg Thieme Verlag Stuttgart · New York

Application of Chemoinformatics in Asymmetric Catalysis

Benjamin List
Joyce A. A. Grimm
Zahrt AF, Henle JJ, Rose BT, Wang Y, Darrow WT, Denmark SE. * University of Illinois, Urbana, USA
Prediction of Higher-Selectivity Catalysts by Computer-Driven Workflow and Machine Learning.

Science 2019;
DOI: 10.1126/science.aau5631.
Further Information

Publication History

Publication Date:
19 March 2019 (online)



Denmark and co-workers report a computer-driven workflow and a machine-learning method that is capable of predicting the enantioinduction of a range of chiral phosphoric acids in N,S-acetal formation. The mathematical model is based on a new descriptor introduced by the authors, i.e. the average steric occupancy, which considers the variability of catalyst conformations and describes points in a 3D space in which a given catalyst resides. The mathematical model is iteratively trained with experimental data, leading to a system that can predict selectivities for catalysts that have not been experimentally tested.



The concept of model-driven method development has far-reaching implications in and beyond the chemical community. However, computer-driven workflows have hitherto not proven capable of predicting experimental results beyond data collected empirically. By describing the population of a catalyst and its conformers in space, a key factor in asymmetric induction, Denmark and co-workers have successfully expanded the utility of such processes, permitting accurate predictions outside the range of the training set. We look forward to the implementation of such a model in systems with greater complexity and in providing solutions to actual problems.