Recent advances in structural biology have led to a rapid accumulation of three-dimensional structural data for biomacromolecules, particularly proteins, enabling highly accurate structure prediction by deep learning– based artificial intelligence. In contrast, reliable structural prediction of glycoproteins remains challenging, largely due to the intrinsic heterogeneity and dynamic conformational behavior of glycans, which have resulted in the limited availability of their three-dimensional structural data. Exploration of glycan conformational space provides a foundation for predicting glycoprotein structure and glycan-dependent cellular functions, thereby extending the central dogma beyond genomic information alone. Decoding the biological information embedded in the four-dimensional structures of glycans requires integrative approaches combining experimental techniques, such as NMR spectroscopy, with computational and information science. The resulting knowledge will advance not only glycoscience and glycoengineering but also drug discovery, materials science, and related fields targeting flexible molecules. Furthermore, extending conformational analysis to non-natural glycans opens new possibilities for molecular design. A major remaining challenge is the development of information science frameworks to interpret dynamic glycan structural data, a goal anticipated within the next decade.