Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction

• URL: http://arxiv.org/abs/2505.23034v1
• Date: Thu, 29 May 2025 03:20:53 GMT
• Title: Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction
• Authors: Guangyi Liu, Yongqi Zhang, Xunyuan Liu, Quanming Yao,
• Abstract summary: We propose CBR-DDI, a novel framework that distills pharmacological principles from historical cases to improve DDI tasks.<n>CBR-DDI constructs a knowledge repository by leveraging LLMs to extract pharmacological insights and graph neural networks (GNNs) to model drug associations.<n>Extensive experiments demonstrate that CBR-DDI achieves state-of-the-art performance, with a significant 28.7% accuracy improvement.
• Score: 34.63988064222427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drug-drug interaction (DDI) prediction is critical for treatment safety. While large language models (LLMs) show promise in pharmaceutical tasks, their effectiveness in DDI prediction remains challenging. Inspired by the well-established clinical practice where physicians routinely reference similar historical cases to guide their decisions through case-based reasoning (CBR), we propose CBR-DDI, a novel framework that distills pharmacological principles from historical cases to improve LLM reasoning for DDI tasks. CBR-DDI constructs a knowledge repository by leveraging LLMs to extract pharmacological insights and graph neural networks (GNNs) to model drug associations. A hybrid retrieval mechanism and dual-layer knowledge-enhanced prompting allow LLMs to effectively retrieve and reuse relevant cases. We further introduce a representative sampling strategy for dynamic case refinement. Extensive experiments demonstrate that CBR-DDI achieves state-of-the-art performance, with a significant 28.7% accuracy improvement over both popular LLMs and CBR baseline, while maintaining high interpretability and flexibility.

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