Scientific article
Open access

Glycoinformatics in the Artificial Intelligence Era

Published inChemical reviews, vol. 122, no. 20, no. Glycosciences, p. 15971-15988
Publication date2022-10-26
First online date2022-08-12

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

Citation (ISO format)
BOJAR, Daniel, LISACEK, Frédérique. Glycoinformatics in the Artificial Intelligence Era. In: Chemical reviews, 2022, vol. 122, n° 20, p. 15971–15988. doi: 10.1021/acs.chemrev.2c00110
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ISSN of the journal0009-2665

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