Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.

聚糖很复杂，但在生物系统中普遍存在。它们参与多种基本的有机体功能。异常的糖基化可能导致疾病的发展，例如癌症、自身免疫性疾病和炎症性疾病。正常和异常的聚糖都是使用广泛的糖基化机制合成的，了解这种机制可以为各种疾病的诊断、预后和治疗提供宝贵的见解。由于糖分析技术的进步，生成的糖组学数据越来越多，但为了最大限度地发挥这些数据的价值，需要在分析和解释大规模糖组学数据方面进行创新。人工智能 (AI) 在许多科学领域提供了强大的分析工具箱，在这里我们回顾一下糖基化分析方面最先进的人工智能方法。我们进一步讨论如何分析模型以获得对糖基化机制的机制见解以及该机制如何在不同情况下塑造聚糖。最后，我们提出如何利用所获得的知识来开发基于人工智能的糖基化预测模型。因此，指导基于人工智能的糖基化模型开发的未来研究将为糖基化和聚糖机制提供有价值的见解。