When East Meets West: Cross-domain Drug Interaction Annotations with Large Language Models and Bidirectional Neural Networks.

Abstract

Drug combination therapy is a promising strategy for managing complex and co-existing diseases. However, drug-drug interactions (DDIs) can result in unexpected adverse effects, making it crucial to understand such interactions to prevent adverse drug reactions and develop new therapeutic strategies. Current DDI annotation methods heavily rely on atom-level graph structural features, overlooking valuable drug contextual representations within retrieval from medical resources. Additionally, these methods are typically designed for a specific task, limiting their scalability to broader medical scenarios. To address these limitations, we propose TEmbed-DDI, a novel framework that leverages meaningful contextual representations and pre-trained large language model embeddings to enhance feature extraction for DDI annotations. Specifically, we retrieve meaningful contextual texts for each drug to enrich semantic features and use pre-trained large language model embeddings to capture rich features from these long-range contextual representations. TEmbed-DDI is the first framework to incorporate LLM-powered embeddings for medical interaction annotations. Furthermore, a bidirectional learning neural network is integrated into TEmbed-DDI for the integrative Western and traditional Chinese medicine DDI annotation tasks. Comparative results demonstrate that TEmbed-DDI achieves state-of-the-art performance, with the highest AUC scores of 0.992 and 0.95 on the Western CHCH and DEEP interaction annotation benchmarks. Even when evaluated on the newly constructed Traditional Chinese Medicine (TCM) DDI annotation benchmark, TEmbed-DDI consistently exhibits outstanding generalization capability, achieving an AUC of 0.956. Moreover, case studies further validate TEmbed-DDI's capability to annotate previously unknown interactions. These findings suggest that TEmbed-DDI can serve as a valuable tool in annotating previously unknown drug combinations for real-world applications, facilitating the development of more effective therapies. Furthermore, as the first framework combining traditional Chinese medicine into DDI annotation tasks, its adaptability highlights potential in supporting cross-domain medical research. TEmbed-DDI's design principles can inspire the development of flexible, LLM-powered frameworks for drug discovery and medical research.