人工智能反映了实验科学，揭示了对细菌进化至关重要的基因转移机制，这一成果由谷歌研究Tiago R.D. Costa课题组经过不懈努力而取得。相关论文于2025年9月9日发表在《细胞》杂志上。
本研究通过提出一个曾耗费多年实验才解决但未发表的科学问题——荚膜形成噬菌体诱导染色体岛（cf-PICIs）如何跨细菌物种传播，来检验最新开发的大型语言模型（LLM）平台“AI合作科学家”生成高水平假说的能力。令人惊讶的是，该AI平台排名首位的假说与我们实验验证的机制完全吻合：cf-PICIs通过劫持多种噬菌体尾部结构来扩展宿主范围。该研究团队批判性地评估了排名最高的五个假设，表明其中一些在他们的实验室开辟了新的研究途径。通过与其他大型语言模型的性能对比，并概述了将人工智能集成到科学发现中的最佳实践。他们的发现表明，人工智能不仅可以作为一种工具，还可以作为一种创造性的引擎，加速科学发现，重塑小组产生和测试科学假设的方式。
据悉，人工智能（AI）模型已经被提出用于生成假设，但测试它们推动高影响力研究的能力是具有挑战性的，因为人工智能生成的假设可能需要数十年的时间才能得到验证。
附：英文原文
Title: AI mirrors experimental science to uncover a mechanism of gene transfer crucial to bacterial evolution
Author: José R. Penadés, Juraj Gottweis, Lingchen He, Jonasz B. Patkowski, Alexander Daryin, Wei-Hung Weng, Tao Tu, Anil Palepu, Artiom Myaskovsky, Annalisa Pawlosky, Vivek Natarajan, Alan Karthikesalingam, Tiago R.D. Costa
Issue&Volume: 2025-09-09
Abstract: Artificial intelligence (AI) models have been proposed for hypothesis generation, but testing their ability to drive high-impact research is challenging since an AI-generated hypothesis can take decades to validate. Here, we challenge the ability of a recently developed large language model (LLM)-based platform, AI co-scientist, to generate high-level hypotheses by posing a question that took years to resolve experimentally but remained unpublished: how could capsid-forming phage-inducible chromosomal islands (cf-PICIs) spread across bacterial species Remarkably, the AI co-scientist’s top-ranked hypothesis matched our experimentally confirmed mechanism: cf-PICIs hijack diverse phage tails to expand their host range. We critically assess its five highest-ranked hypotheses, showing that some opened new research avenues in our laboratories. We benchmark its performance against other LLMs and outline best practices for integrating AI into scientific discovery. Our findings suggest that AI can act not just as a tool but as a creative engine, accelerating discovery and reshaping how we generate and test scientific hypotheses.
DOI: 10.1016/j.cell.2025.08.018
Source: https://www.cell.com/cell/abstract/S0092-8674(25)00973-0