AI mirrors experimental science to uncover a novel mechanism of gene transfer crucial to bacterial evolution

Note this is a re-submission of a previously approved ITP. The previous approval was conditional for a journal pre-sub enquiry only and we are submitting a new ITP for the preprint of the paper.


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 LLM-based platform 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experimentally but remained unpublished: How could capsid-forming phage-inducible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’s top-
ranked hypothesis matched our experimentally confirmed mechanism: cf-PICIs hijack
diverse phage tails to expand their host range. We critically assess the AI’s 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 computational
tool, but as a creative engine, accelerating discovery and reshaping how we generate and
test scientific hypotheses.