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

April 4, 2023
Featured Article

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.  

For details

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

For more information about Chemspeed solutions:

FLEX ISYNTH

Contact us to learn more about this exciting article:

https://www.chemspeed.com/contact-us/

Other Recent News

Discover more news articles you might be interested in

Read more about High-throughput solid microsampling through stochastic robotic automation
News Picture 1 1 V2
Featured
Jul
14

High-throughput solid microsampling through stochastic robotic automation

Solid sampling at the sub-milligram and milligram scale remains a major bottleneck for high-throughput chemistry or material sciences, as existing approaches rely on manual handling or slow deterministic microsampling that do not readily scale.

Here we present STORMS, an automated STOchastic Robotic MicroSampling system that enables fast, reliable and parallel sampling of solid materials at sub-milligram and milligram masses. 

Read more about Parameter efficient multi-model vision assistant for polymer solvation behaviour inference
News Picture 1 1 V2
Jun
30

Parameter efficient multi-model vision assistant for polymer solvation behaviour inference

Polymer–solvent systems exhibit complex solvation behaviours encompassing a diverse range of phenomena, including swelling, gelation, and dispersion. Accurate interpretation is often hindered by subjectivity, particularly in manual rapid screening assessments. While computer vision models hold significant promise to replace the reliance on human evaluation for inference, their adoption is limited by the lack of domain-specific datasets tailored, in our case, to polymer–solvent systems.

Read more about Continuous Flow Synthesis of Diglycolamides for Rare-Earth Elements Recovery: Algorithm-Accelerated Reaction Optimization Coupled with Automated Extraction Evaluation
News Picture 1 1 V2
Featured
Jun
23

Continuous Flow Synthesis of Diglycolamides for Rare-Earth Elements Recovery: Algorithm-Accelerated Reaction Optimization Coupled with Automated Extraction Evaluation

A first example of diglycolamide flow synthesis was developed, showcasing algorithm-accelerated reaction optimization and its potential to accelerate ligand discovery and enable autonomous, AIcontrolled manufacturing of critical materials. The increasing demand for manufacturing rare-earth elements (REEs) as critical materials has intensified research on sustainable synthesis of advanced extractants such as diglycolamides (DGAs).

© Chemspeed (part of Bruker) 2026