# Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment

arXiv: https://arxiv.org/abs/2405.12258 · HTML: https://mlsanity.com/paper/2405.12258/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Accelerating science with human-aware artificial intelligence](https://mlsanity.com/paper/2306.01495/) (2023)
- [The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence](https://mlsanity.com/paper/2307.07522/) (2023)
- [Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidata](https://mlsanity.com/paper/2309.08491/) (2023)

## Built on by (most cited first, 11 of 11)

- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance](https://mlsanity.com/paper/2506.12937/) (2025)
- [34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery](https://mlsanity.com/paper/2505.03049/) (2025)
- [Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models](https://mlsanity.com/paper/2505.14599/) (2025)
- [Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2411.15221/) (2024)
- [Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery](https://mlsanity.com/paper/2505.16477/) (2025)
- [SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?](https://mlsanity.com/paper/2604.10718/) (2026)
- [Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing](https://mlsanity.com/paper/2511.12529/) (2025)
- [Symbolic Neural Generation with Applications to Lead Discovery in Drug Design](https://mlsanity.com/paper/2510.23379/) (2025)
- [LapidaryEngine: Fully Conversational Crystal Generation](https://mlsanity.com/paper/2606.14215/) (2026)
- [Large Language Models Extend Known Research But Do Not Create New Research Directions](https://mlsanity.com/paper/2610.17439/) (2026)

## Abstract (arXiv)

Large language models LLMs have transformed 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 FDA-approved noncancer drugs that target the MCF7 breast cancer cell line relative to the nontumorigenic breast cell line MCF10A In the first round of laboratory experiments GPT4 succeeded in discovering three drug combinations out of twelve 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.