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

TL;DR

This paper introduces CBR-DDI, a novel framework that combines case-based reasoning with large language models and graph neural networks to significantly improve drug-drug interaction prediction accuracy.

Contribution

The paper presents a new CBR-based framework that distills pharmacological knowledge from historical cases to enhance LLM reasoning in DDI prediction, achieving state-of-the-art results.

Findings

28.7% accuracy improvement over baseline models

Effective retrieval and reuse of relevant cases in DDI prediction

Maintains high interpretability and flexibility

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.