Motivation This study aims to develop an AI-driven framework that leverages large language models (LLMs) to simulate scientific reasoning and peer review to predict efficacious combinatorial therapy when data-driven prediction is infeasible. Results Our proposed framework achieved a significantly higher accuracy (0.74) than traditional knowledge-based prediction (0.52). An ablation study highlighted the importance of high quality few-shot examples, external knowledge integration, self-consistency, and review within the framework. The external validation with private experimental data yielded an accuracy of 0.82, further confirming the framework's ability to generate high-quality hypotheses in biological inference tasks. Our framework offers an automated knowledge-driven hypothesis generation approach when data-driven prediction is not a viable option. Availability and implementation Our source code and data are available at https://github.com/QidiXu96/Coated-LLM

The authors have declared no competing interest.

This work was supported in part by National Institute of Health (NIH) under award number R01AG082721, R01AG066749, and R01AG084637.

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