虽然AlphaFold因蛋白质结构预测能力在2024年获得诺贝尔化学奖，但这一成功的条件极其苛刻且难以复制[1]。蛋白质数据库的建立耗时53年、投入约210亿美元的实验工作，而AlphaFold的突破依赖于约170,000个经实验验证的蛋白质结构数据集[1]。这样的大规模、高质量数据集在科学领域极为罕见。
鉴于这些限制，业界认为AI代理（agents）才是加速科学研究的更广泛且可行的模式[1]。这类工具能够整合多个科学工具、自动记录方法过程、加速实验速度[1]。Google的AI Co-Scientist已展现这一潜力，在一项研究中仅用一天时间得出与伦敦帝国理工学院十年研究相同的结论[1]。通过自动记录每一步操作，AI代理能创建精确的方法记录以支持完全复制[1]，这有望解决科学的可重复性危机并改变科学研究的节奏[1]。
While AlphaFold's achievement in protein structure prediction earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry, the conditions for its success are rare and difficult to replicate [1]. The breakthrough relied on approximately 170,000 experimentally verified protein structures, data that took 53 years to accumulate and required roughly $21 billion in experimental work to generate [1].
Rather than pursuing similar data-intensive approaches, AI agents represent a more scalable pattern for accelerating scientific progress [1]. These tools integrate multiple scientific instruments, automatically document methodologies at each step, and enable faster experiment cycles [1]. Google's AI Co-Scientist demonstrated this potential by reaching conclusions in a single day that matched a decade of research by Imperial College London [1]. By creating precise method records through automated logging, AI agents could address the reproducibility crisis in science and fundamentally alter the pace of scientific discovery [1].