ArticleJournal of the Royal Society, Interface2025
Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.
Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Pooled it
- Article
- Capacity of Understanding the Future Approaches in Cancer Treatment by Multiple Models of Artificial Intelligence.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- AI for clinical trials in oncology.ESMO real world data and digital oncology · 2026Review
- The future of fundamental science led by generative closed-loop artificial intelligence.Frontiers in artificial intelligence · 2026Review
- A case study of the application of AI to early stage drug discovery.Scientific reports · 2025Article
- A Process-Centric Survey of AI for Scientific Discovery Through the EXHYTE Framework.Research square · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- 32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.Machine learning: science and technology · 2025Article
- Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.Journal of the Royal Society, Interface · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.