Bayesian optimization for enzyme-catalyzed, site-selective acetylation of carbohydrate OH groups
- 1Department of Chemistry, University of Toronto 80 St. George St. Toronto Ontario M5S 3H6 Canada
- 2Department of Chemistry, University of British Columbia 2036 Main Mall Vancouver British Columbia V6T 1Z1 Canada
Abstract
In this study, we employ Bayesian optimization to identify conditions for enzyme-catalyzed, site-selective esterifications of pyranoside derivatives. Our findings indicate that Bayesian algorithms facilitate these challenging optimizations, which involve simultaneous variation of enzyme identity, solvent, acyl donor loading, time and temperature, across a range of substrates with various configurations, protective groups and anomeric substituents. Using the experimental optimization data obtained for eighteen pyranoside substrates, we evaluate models for yield prediction and explore approaches for ‘warm-starting’ optimizations of previously unseen substrates. Products of the method are applied in photocatalytic hydrogen atom transfer reactions to generate ketodeoxy sugars.
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BibTeX
@article{ruzicka2026bayesian,
title = {{Bayesian optimization for enzyme-catalyzed, site-selective acetylation of carbohydrate OH groups}},
author = {Katrina H. Ruzicka and Matthew T. Zambri and Jolene P. Reid and Mark S. Taylor},
journal = {Chemical Science},
year = {2026},
month = nov,
doi = {10.1039/d6sc06368c},
url = {https://doi.org/10.1039/d6sc06368c},
note = {CC BY 3.0},
}
RIS
TY - JOUR
TI - Bayesian optimization for enzyme-catalyzed, site-selective acetylation of carbohydrate OH groups
AU - Katrina H. Ruzicka
AU - Matthew T. Zambri
AU - Jolene P. Reid
AU - Mark S. Taylor
PY - 2026
DA - 2026/11/04
JO - Chemical Science
DO - 10.1039/d6sc06368c
UR - https://doi.org/10.1039/d6sc06368c
AB - In this study, we employ Bayesian optimization to identify conditions for enzyme-catalyzed, site-selective esterifications of pyranoside derivatives. Our findings indicate that Bayesian algorithms facilitate these challenging optimizations, which involve simultaneous variation of enzyme identity, solvent, acyl donor loading, time and temperature, across a range of substrates with various configurations, protective groups and anomeric substituents. Using the experimental optimization data obtained for eighteen pyranoside substrates, we evaluate models for yield prediction and explore approaches for ‘warm-starting’ optimizations of previously unseen substrates. Products of the method are applied in photocatalytic hydrogen atom transfer reactions to generate ketodeoxy sugars.
SN - 2041-6520
SN - 2041-6539
ER -