AI mirrors experimental science to uncover a mechanism of gene transfer crucial to bacterial evolution
File(s)
Published version
Author(s)
Type
Journal Article
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to generate high-level hypotheses by posing a question that took years to resolve experimentally but remained unpublished: how could capsid-forming phage-inducible chromosomal islands (cf-PICIs) spread across bacterial species?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.
Date Issued
2025-11-13
Date Acceptance
2025-08-13
Citation
Cell, 2025, 188 (23), pp.6654-6665.E2
ISSN
0092-8674
Publisher
Elsevier BV
Start Page
6654
End Page
6665.E2
Journal / Book Title
Cell
Volume
188
Issue
23
Copyright Statement
© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.cell.2025.08.018
Publication Status
Published
Date Publish Online
2025-09-09