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 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.
Highlights
Predicting the outcome of an organic reaction generally requires a detailed understanding of the steric and electronic factors in uencing the potential energy[1,2] surface[3] and intermediate(s).[4]
We have trained a random forest algorithm using a dataset of glycosylation reactions with a variety of stereoselective outcomes to accurately predict the stereoselectivity of new glycosylations, varying coupling partners, acid catalyst, solvents, and temperature
Regressionbased random forest algorithms have proven powerful in modeling chemical reaction performance.[10,27]
Summary
Predicting the outcome of an organic reaction generally requires a detailed understanding of the steric and electronic factors in uencing the potential energy[1,2] surface[3] and intermediate(s).[4]. A random forest algorithm was trained using a highly reproducible, concise dataset to accurately predict the stereoselective outcome of glycosylations. 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%).
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