Subscribe to RSS
DOI: 10.1055/s-0037-1612338
Application of Chemoinformatics in Asymmetric Catalysis
Prediction of Higher-Selectivity Catalysts by Computer-Driven Workflow and Machine Learning.
Science 2019;
363
DOI: 10.1126/science.aau5631.
Publication History
Publication Date:
19 March 2019 (online)
Key words
chiral phosphoric acids - catalyst design - chemoinformatics - acetals - artificial intelligence![](https://www.thieme-connect.de/media/synfacts/201904/i_b015_s1_10-1055_s-0037-1612338.gif)
Significance
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.
#
Comment
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.
#
#
![](https://www.thieme-connect.de/media/synfacts/201904/i_b015_s1_10-1055_s-0037-1612338.gif)