Substrate specific closed-loop optimization of carbohydrate protective group chemistry using bayesian optimization and transfer learning

ChemRxiv

A new way of performing reaction optimization within carbohydrate chemistry is presented.  This is done by performing closed-loop optimization of regioselective benzoylation of unprotected glycosides using Bayesian optimization. Both 6-O-monobenzoylations and 3,6-O-dibenzoylations of three different monosaccharides are optimized. A novel transfer learning approach, where data from previous optimizations of different substrates is used to speed up the optimizations, has also been developed. The optimal conditions found by the Bayesian Optimization algorithm provide new insight into substrate specificity, as the conditions found are significantly different. In most cases, the optimal conditions include Et3N and benzoic anhydride, a new reagent combination for these reactions, discovered by the algorithm, demonstrating the power of this concept to widen the chemical space. Further, the developed procedures include ambient conditions and short reaction times.  

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Substrate specific closed-loop optimization of carbohydrate protective group chemistry using bayesian optimization and transfer learning 

Natasha Videcrantz Faurschou a, Rolf Hejle Taaning b, and Christian Marcus Pedersen a

a. Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark
b. LEO Pharma A/S, Industriparken 55, 2750 Ballerup, Denmark, Previous affiliation: Novo Nordisk A/S, Smørmosevej 17-19, 2880 Bagsværd, Denmark

ChemRxiv
DOI: 10.26434/chemrxiv-2023-1pm0d

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