Journal of Agricultural Big Data >

2025 , Vol. 7 >Issue 4: 485 - 495

DOI: https://doi.org/10.19788/j.issn.2096-6369.000136

Unveiling AlphaFold’s Iterative Breakthroughs: Data Strategy Insights from a Scientific Perspective

  • OUYANG ZhengZheng ,
  • MA YuCong ,
  • KOU YuanTao ,
  • XIAN GuoJian ,
  • WANG Hui ,
  • ZHAO Qun
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  • 1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2. National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu 610299, China
    3. Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, Beijing 100081, China
    4. National Science Library, Chinese Academy of Sciences, Beijing 100190, China
    5. Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China

Received date: 2025-10-14

  Revised date: 2025-11-09

  Online published: 2025-12-26

Fold

Abstract

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.

Key words： AlphaFold; scientific data; data-model synergy; protein structure prediction; AI for science

Cite this article

OUYANG ZhengZheng , MA YuCong , KOU YuanTao , XIAN GuoJian , WANG Hui , ZHAO Qun . Unveiling AlphaFold’s Iterative Breakthroughs: Data Strategy Insights from a Scientific Perspective[J]. Journal of Agricultural Big Data, 2025 , 7(4) : 485 -495 . DOI: 10.19788/j.issn.2096-6369.000136

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