These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
6 files

Predicting Glycosylation Stereoselectivity Using Machine Learning

submitted on 23.05.2020, 10:59 and posted on 26.05.2020, 08:03 by Soo-Yeon Moon, Sourav Chatterjee, Peter H. Seeberger, Kerry Gilmore
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 discovered that environmental factors influence the stereoselectivity of glycosylations more than the coupling partners in this area of chemical space.


DFG (FOR 2177)


Email Address of Submitting Author


Max-Planck Institute of Colloids and Interfaces



ORCID For Submitting Author


Declaration of Conflict of Interest

None of the authors declare a conflict of interest

Version Notes

Version 1.0