AlphaFold系列模型在结构生物学领域的革命性突破常被归因于算法创新，但其背后更为根本的科学数据策略演进却鲜有系统性剖析。本文从科学数据的核心视角出发，系统解构AlphaFold 1至3代的迭代突破机制，聚焦数据内在属性优化、表征范式革新、数据-模型协同适配三大关键层面，论证模型每一次性能跃升的本质均是数据-模型协同进化的结果。研究揭示：AlphaFold的演进是其数据策略从被动沿用、主动构建到生成赋能的历程。基于此，本文提炼出三大核心规律：表征范式的跃迁是突破的核心驱动，数据-模型的协同演进是成熟的关键标志，而数据内在属性的丰富度则决定了AI学习范式的上限。这些规律为AI for Science（AI4S）领域带来四大关键启示：数据工作需从被动准备转向主动设计；研发应从“模型/数据中心”转向以“契合度”为中心；数据体系构建应靶向提升核心属性而非盲目多模态聚合；业界亟待构建一套衡量数据“科学效能”的全新理论与评估框架，为AI驱动的科学发现提供理论支撑与路径参考。

The transformative breakthroughs of the AlphaFold series in structural biology are often attributed to algorithmic advances, yet the critical role of its evolving data strategy remains underexplored. Adopting a data-centric perspective, this paper deconstructs the iterative mechanisms driving AlphaFold’s progress from versions 1 to 3, emphasizing the optimization of data quality attributes, innovations in representation paradigms, and data-model synergy. The analysis reveals that each performance leap stems from the co-evolution of data and model architectures. AlphaFold’s data strategy follows a clear trajectory: from passive data adoption, to proactive data construction, and finally to generative data augmentation. From this, three core principles emerge: paradigm shifts in data representation are the primary drivers of breakthroughs; data-model co-evolution is a hallmark of system maturity; and the richness of data quality attributes sets the ceiling for an AI’s learning potential. These principles yield four implications for the AI for Science (AI4S) field: data practices should shift from passive preparation to active design; research should prioritize data-model alignment over model- or data-centric approaches; data ecosystems should focus on enhancing key attributes, such as diversity and quality, rather than broad multimodal integration; and a new theoretical and evaluation framework is needed to assess the "scientific efficacy" of data. This study provides a theoretical foundation and practical roadmap for advancing AI-driven scientific discovery.