[The development of AlphaFold and its applications in biology and medicine].

The emergence of AlphaFold has catalyzed a paradigm shift in protein structure prediction, redefining the landscape of computational biology through its iterative evolution. The developmental trajectory spans three transformative iterations: the foundational AlphaFold prototype, its revolutionary successor AlphaFold2, and the recently unveiled AlphaFold3. AlphaFold2 marked a quantum leap in 2020 by introducing an end-to-end deep learning architecture that achieved atomic-level accuracy, decisively solving the decades-old protein folding problem as demonstrated by its unprecedented performance at CASP14 (Critical Assessment of Structure Prediction). Building upon this framework, AlphaFold3 represents an evolutionary leap, expanding predictive capabilities to model intricate biomolecular complexes including ligand-protein binding interfaces and nucleic acid interactions.These advancements have unlocked transformative applications across multiple domains: enabling rapid proteome-scale structural annotations in structural biology, accelerating virtual screening pipelines in drug discovery, and facilitating viral protein characterization in emerging virology research. However, persistent limitations in modeling conformational dynamics and transient binding states underscore the need for continued methodological refinement. This comprehensive analysis examines the algorithmic innovations driving AlphaFold's progression, evaluates its multidisciplinary applications, and critically assesses current technical constraints-providing a framework to guide future developments at the intersection of artificial intelligence and molecular bioscience.