2026Li Atomic

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Revision as of 07:26, 18 February 2026 by WikiSysop (talk | contribs) (Created page with "== Citation == Li, T. and Huang, S.-Y. 2026. Deep learning–based postprocessing and model building for cryo-electron microscopy maps. Current Opinion in Structural Biology. 96, (2026), 103196. == Abstract == Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and...")
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Citation

Li, T. and Huang, S.-Y. 2026. Deep learning–based postprocessing and model building for cryo-electron microscopy maps. Current Opinion in Structural Biology. 96, (2026), 103196.

Abstract

Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and atomic-model building are two crucial final steps of the cryo-EM pipeline. With the fast development of artificial intelligence, deep learning has been implemented in various stages of cryo-EM. Here, we present a comprehensive overview of recent advances in map postprocessing and model building for cryo-EM maps with focuses on deep learning–based methods. We also discuss the advantages and limitations of current approaches as well as challenges that are left for future research.

Keywords

https://www.sciencedirect.com/science/article/pii/S0959440X25002143

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