AlphaFold 3: an unprecedent opportunity for fundamental research and drug development

Ziqi Fang , Hongbiao Ran , YongHan Zhang , Chensong Chen , Ping Lin , Xiang Zhang , Min Wu

Precision Clinical Medicine ›› 2025, Vol. 8 ›› Issue (3) : pbaf015

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Precision Clinical Medicine ›› 2025, Vol. 8 ›› Issue (3) : pbaf015 DOI: 10.1093/pcmedi/pbaf015
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AlphaFold 3: an unprecedent opportunity for fundamental research and drug development

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Abstract

AlphaFold3 (AF3), as the latest generation of artificial intelligence model jointly developed by Google DeepMind and Isomorphic Labs, has been widely heralded in the scientific research community since its launch. With unprecedented accuracy, the AF3 model may successfully predict the structure and interactions of virtually all biomolecules, including proteins, ligands, nucleic acids, ions, etc. By accurately simulating the structural information and interactions of biomacromolecules, it has shown great potential in many aspects of structural prediction, mechanism research, drug design, protein engineering, vaccine development, and precision therapy. In order to further understand the characteristics of AF3 and accelerate its promotion, this article sets out to address the development process, working principle, and application in drugs and biomedicine, especially focusing on the intricate differences and some potential pitfalls compared to other deep learning models. We explain how a structure-prediction tool can impact many research fields, and in particular revolutionize the strategies for designing of effective next generation vaccines and chemical and biological drugs.

Keywords

AlphaFold3 / artificial intelligence / structure prediction / drug design / biomedical research

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Ziqi Fang, Hongbiao Ran, YongHan Zhang, Chensong Chen, Ping Lin, Xiang Zhang, Min Wu. AlphaFold 3: an unprecedent opportunity for fundamental research and drug development. Precision Clinical Medicine, 2025, 8(3): pbaf015 DOI:10.1093/pcmedi/pbaf015

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Acknowledgments

This work was supported by funds of the Key Research and Development Grant of MOST (grant No. 2023YFA095000), the Affiliated Xiangshan Hospital of Wenzhou Medical University, the National Science Foundation of China (grant No. 82470005), and Wenzhou Institute University of Chinese Academy of Sciences. We thank members of the laboratory for helpful discussion.

Author contributions

Ziqi Fang (Writing—original draft), Hongbiao Ran (Writing—original draft), Yonghan Zhang (Writing—original draft), Chensong Chen (Writing—review & editing), Ping Lin (Writing—review & editing), Xiang Zhang (Writing—review & editing), and Min Wu (Writing—review & editing).

Conflict of interest

None declared. The corresponding author M.W. as an Editorial Board Member of Precision Clinical Medicine was blinded from reviewing and making decisions on this manuscript.

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