Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction
This work addresses drug safety for patients and clinicians by improving DDI prediction, though it is incremental as it builds on existing LLM and CBR methods.
The paper tackled the problem of drug-drug interaction (DDI) prediction by proposing CBR-DDI, a framework that uses case-based reasoning to enhance large language models (LLMs), resulting in a 28.7% accuracy improvement over baselines.
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.