Predicting Glycosylation Stereoselectivity Using Machine Learning

26 May 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

Stereochemistry
Machine Learning
Glycosylation
Prediction

Supplementary materials

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gly stereo predict SI final
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Total 342 data
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training 268
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validation 74
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Potential descriptors
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