Scientific hypothesis generation by large language models: Laboratory validation in breast cancer treatment
Journal article, 2025
Large language models (LLMs) have transformed artificial intelligence (AI) and achieved breakthrough performance on a wide range of tasks. In science, the most interesting application of LLMs is for hypothesis formation. A feature of LLMs, which results from their probabilistic structure, is that the output text is not necessarily a valid inference from the training text. These are termed 'hallucinations', and are harmful in many applications. In science, some hallucinations may be useful: novel hypotheses whose validity may be tested by laboratory experiments. Here, we experimentally test the application of LLMs as a source of scientific hypotheses using the domain of breast cancer treatment. We applied the LLM GPT4 to hypothesize novel synergistic pairs of US Food and Drug Administration (FDA)-approved non-cancer drugs that target the MCF7 breast cancer cell line relative to the non-tumorigenic breast cell line MCF10A. In the first round of laboratory experiments, GPT4 succeeded in discovering three drug combinations (out of 12 tested) with synergy scores above the positive controls. GPT4 then generated new combinations based on its initial results, this generated three more combinations with positive synergy scores (out of four tested). We conclude that LLMs are a valuable source of scientific hypotheses.
drug discovery
artificial intelligence for science
machine learning
cancer research
personalized medicine
Author
Abbi Abdel-Rehim
University of Cambridge
Hector Zenil
Alan Turing Institute
The Francis Crick Institute
University of Oxford
King's College London
University of Cambridge
Oghenejokpeme I. Orhobor
University of Cambridge
Marie Fisher
Arctoris
Ross J. Collins
Arctoris
Elizabeth Bourne
Arctoris
Gareth W. Fearnley
Arctoris
Emma Tate
Arctoris
Holly X. Smith
Arctoris
Larisa N. Soldatova
Goldsmiths, University of London
Ross King
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
University of Gothenburg
University of Cambridge
Journal of the Royal Society Interface
1742-5689 (ISSN) 1742-5662 (eISSN)
Vol. 22 227 20240674Subject Categories (SSIF 2025)
Cancer and Oncology
DOI
10.1098/rsif.2024.0674