AI mirrors experimental science to uncover a novel mechanism of gene transfer crucial to bacterial evolution
Open accessPreprint — published as 10.1016/j.cell.2025.08.018 · Paper page
Abstract
SUMMARY 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, 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to generate high-level hypotheses by posing a question that took years to resolve experimentally but remained unpublished: How could capsid-forming phage-inducible chromosomal islands (cf-PICIs) spread across bacterial species?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, 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.
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LLMs have rediscovered established findings and generated novel hypotheses worthy of investigation across diverse fields.
“They have rediscovered established findings [12] and generated novel hypotheses worthy of investigation across diverse fields [13-15].”
From When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research (2025) · cites this paper as [12]
AI co-scientist came up with the answer that Penadés et al. had experimentally verified but not yet published, in two days.
“In two days, AI co-scientist came up with the answer that Penadés et al. had experimentally verified but not yet published, which meant that the AI system should not have had access to their paper [16].”
From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [16]
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- Authors
- Penadés, J. R., Gottweis, J., He, L., Patkowski, J. B., Shurick, A., Weng, W.-H., Tu, T., Palepu, A., Myaskovsky, A., Pawlosky, A., Natarajan, V., Karthikesalingam, A., & Costa, T. R. D.
- Affiliations
- Universidad Cardenal Herrera CEU (ES)
- Google (United States) (US)
- Imperial College London (GB)
- Google Research, Mountain View, CA, USA
- Corresponding author
- Penadés, José R
- Natarajan, Vivek
- Karthikesalingam, Alan
- Costa, Tiago R D
- ORCID
- Venue
- Cell
- Publisher
- openRxiv
- Published
- 2025-02-19
- Volume
- 188
- Issue
- 23
- Pages
- 6654-6665.e2
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W4414152979
- Cited by
- 42
- References
- 0
- Citations by year
- 2026: 31 · 2025: 11
- FWCI
- 22.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Directorate for Biological Sciences
- Medical Research Council (MR/S00940X/2, BB/V009583/1, BB/V002376/1, MR/S00940X/1, MR/X020223/1, MR/V000772/1, MR/M003876/1)
- Engineering and Physical Sciences Research Council (EP/X026671/1)
- Biotechnology and Biological Sciences Research Council (BB/V009583/1, BB/V002376/1, EP/X026671/1)
- European Research Council (215164/Z/18/Z/WT, 101118890)
- Topics
- Physical Sciences › Environmental Science › Ecology › Bacteriophages and microbial interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- large language models, gene transfer, bacterial evolution, host range expansion, hypothesis testing
- MeSH terms
- Artificial Intelligence, Bacteria, Bacteriophages, Evolution, Molecular, Gene Transfer, Horizontal, Genomic Islands
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