Anomeric Selectivity of Glycosylations Through a Machine Learning Lens

2024/11/14 by Natasha Videcrantz Faurschou, Victor Friis, Priyanka Raghavan +2 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Machine Learning in Bioinformatics #Glycosylation and Glycoproteins Research

paper · pdf · doi:10.26434/chemrxiv-2024-jw9dx

openalex publication_date 2024/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/15

Abstract

Predicting the stereoselectivity of glycosylations is a major challenge in carbohydrate chemistry. Herein we show that it is possible to build machine learning models that can predict the major anomer of a glycosylation, whether the other anomer is observed as the minor product, and the anomeric ratio of the two anomers. The three models are integrated into a publicly available tool, GlycoPredictor. From a statistical analysis of literature data, we analyze glycosylation trends and compare them to known trends in the field of carbohydrate chemistry, making it possible to elucidate a hierarchy of rules governing the stereoselectivity of glycosylations and discover promising new trends that complement expert intuition.

Discussions

• #RobSelects preprint of the week #ChemRxiv: Predicting anomeric stereoselectivity of glycosylation reactions with machine learning. #aichem https://doi.org/10.26434/chemrxiv-2024-jw9dx [bsky, 2 points, 0 comments]

Related

• Direct imaging of carbohydrate stereochemistry
• GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks
• Predicting the Stereoselectivity of Chemical Transformations by Machine Learning
• GELATO and SAGE: An Integrated Framework for MS Annotation
• Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks