Title: Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: LLM, Drug-Drug Interaction, Case-Based Reasoning

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 patterns 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 two-tier 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.

TL;DR: We propose CBR-DDI, a novel framework that distills pharmacological patterns from historical cases to improve LLM reasoning for Drug-Drug Interaction tasks.