Issue 8, 2021

Predicting glycosylation stereoselectivity using machine learning


Predicting the stereochemical outcome of chemical reactions is challenging in mechanistically ambiguous transformations. The stereoselectivity of glycosylation reactions is influenced by at least eleven factors across four chemical participants and temperature. A random forest algorithm was trained using a highly reproducible, concise dataset to accurately predict the stereoselective outcome of glycosylations. The steric and electronic contributions of all chemical reagents and solvents were quantified by quantum mechanical calculations. The trained model accurately predicts stereoselectivities for unseen nucleophiles, electrophiles, acid catalyst, and solvents across a wide temperature range (overall root mean square error 6.8%). All predictions were validated experimentally on a standardized microreactor platform. The model helped to identify novel ways to control glycosylation stereoselectivity and accurately predicts previously unknown means of stereocontrol. By quantifying the degree of influence of each variable, we begin to gain a better general understanding of the transformation, for example that environmental factors influence the stereoselectivity of glycosylations more than the coupling partners in this area of chemical space.

Graphical abstract: Predicting glycosylation stereoselectivity using machine learning

Supplementary files

Article information

Article type
Edge Article
11 Nov 2020
24 Dec 2020
First published
26 Dec 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2021,12, 2931-2939

Predicting glycosylation stereoselectivity using machine learning

S. Moon, S. Chatterjee, P. H. Seeberger and K. Gilmore, Chem. Sci., 2021, 12, 2931 DOI: 10.1039/D0SC06222G

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