Substrate Specific Closed-loop Optimization of Carbohydrate Protective Group Chemistry Using Bayesian Optimization and Transfer Learning

23 February 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


closed-loop optimization
transfer learning
carbohydrate chemistry
Bayesian optimization
substrate specificity
protecting group

Supplementary materials

Supporting Information
Experimental procedures, NMR, Benchmark study, robot script
All Closed-loop experiments
Excel document with all closed-loop conditions and yields


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