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.

中文翻译：

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人工智能时代的糖信息学

人工智能 (AI) 方法已经并且现在越来越多地集成到生物信息学及其糖科学分支（称为糖信息学）中实施的预测软件中。人工智能技术在过去几十年里不断发展，但其在糖科学中的应用尚未广泛。这种有限的使用部分是由于糖数据的特殊性造成的，众所周知，糖数据很难产生和分析。尽管如此，随着时间的推移，糖组学、糖蛋白组学和聚糖结合数据的积累已经达到了这样的程度，即使是最新的深度学习方法也可以为预测器提供良好的性能。我们讨论了各种人工智能方法在更广泛的糖信息学领域应用的历史发展。特别重点关注糖数据处理方面的挑战，并结合相关学科的经验教训。在结束对糖信息学领域最先进的深度学习方法的讨论后，我们还展望了糖信息学的未来，包括为了真正释放系统生物学时代糖科学的能力而需要进行的发展。