Proteins, nucleic acids, and other biomacromolecules are the fundamental substances that constitute life. The structure of biomacromolecules serves as a critical foundation for studying the mechanisms of disease occurrence and drug action, and provides an important reference for drug discovery. With the continuous development of structural biology, genomics, proteomics, etc., substantial data on biomacromolecular structures and activity have been generated, especially the three-dimensional structures of proteins, nucleic acids, and other biomacromolecules, which constitute valuable resources for research in life sciences and medical fields. To better manage and use these data resources, researchers have organized and established various types of databases of biomacromolecular structures to meet different purposes and needs. In addition, researchers have developed valuable data resources and application tools based on the original structural data, such as homology modeling tools and visualization analysis tools, facilitating users to further analyze and process the original data, thereby reducing barriers to entry. Recently, artificial intelligence (AI) technology has made breakthrough advancements in the field of protein structure prediction. The developed artificial intelligence models can accurately predict the three-dimensional structure of proteins and their complexes, significantly enriching the resources of biomacromolecular structure data.