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

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to generate high-level hypotheses by posing a question that took years to resolve experimentally but remained unpublished: how could capsid-forming phage-inducible chromosomal islands (cf-PICIs) spread across bacterial species?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.

Keywords: AI-driven discovery; bacterial evolution; cf-PICIs; gene transfer; inter-species transfer; large language models; tail hijacking.