Elizabeth Bourne | Searcharxiv

arXiv2026

Iterative AI-guided optimisation of selective triple-drug combinations for breast cancer

Personalised cancer therapy aims to tailor treatment to individual tumour profiles, yet tumour heterogeneity and adaptive resistance continue to limit clinical efficacy. Drug combinations offer a strategy to overcome resistance by simultaneously targeting multiple pathways, but their rational design is constrained by the vast combinatorial search space and experimental cost. Here, we present an AI-guided, QSAR-driven iterative optimisation framework that integrates machine learning with automated experimental screening to enable closed-loop discovery of selective multi-drug therapies. Starting from an initial random screen, the system iteratively predicts, tests, and refines three-drug combinations targeting MCF7 breast cancer cells. Incorporation of non-tumorigenic MCF10A cells enables explicit optimisation of tumour-selective efficacy, prioritising regimens that maximise cancer cell killing while sparing healthy cells. Across successive iterations, the framework rapidly enriched for highly selective, high-efficacy combinations, while maintaining chemical and mechanistic diversity and avoiding convergence on a narrow solution space. By continuously learning from experimental feedback, the approach efficiently navigates millions of combinations to identify a small set of validated, tumour-selective regimens. These results establish a scalable proof-of-concept for AI-driven, closed-loop optimisation of higher-order drug combinations, demonstrating how iterative integration of computation and experimentation can enable adaptive and potentially personalised therapeutic design in precision oncology.

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arXiv2024

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

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.

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