农业大数据学报2025，Vol.7Issue(4)：485-495,11.DOI:10.19788/j.issn.2096-6369.000136
科学数据视角下AlphaFold的迭代突破与数据策略启示
Unveiling AlphaFold's Iterative Breakthroughs:Data Strategy Insights from a Scientific Perspective
摘要
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.关键词
AlphaFold/科学数据/数据-模型协同/蛋白质结构预测/AI驱动科学发现Key words
AlphaFold/scientific data/data-model synergy/protein structure prediction/AI for science引用本文复制引用
欧阳峥峥,马毓聪,寇远涛,鲜国建,王辉,赵群..科学数据视角下AlphaFold的迭代突破与数据策略启示[J].农业大数据学报,2025,7(4):485-495,11.基金项目
2024年度国家新闻出版署农业融合出版知识挖掘与知识服务重点实验室开放课题基金资助项目(2024KMKS05) （2024KMKS05）
中国科学院成都文献情报中心2023年度创新基金重点项目(E3Z0000901) （E3Z0000901）
