BIOFUSE-DDI: A Dual-Source Transformer Framework for Drug-Drug Interaction Prediction

Hengpeng Zhao, Shuoyu Cui, Xiaoli Lin, Ping Liang

Abstract: Drug-Drug Interactions (DDIs) prediction is crucial for drug safety and clinical decision-making. While existing computational methods have made progress in DDI prediction, they often fail to fully integrate multiple drug features, particularly the biological relevance from protein sequences. In this paper, we propose BIOFUSE-DDI, a novel dual-source transformer framework that effectively combines knowledge graph information with protein sequence features for DDI prediction. Our model incorporates three key components: a Dual-Source Attention Mechanism for feature extraction, a Bimodal Interaction Enhancement module for feature fusion, and a Sequential Transformer Refinement for comprehensive interaction pattern learning. Extensive experiments on both binary-class and multi-class DDI prediction tasks demonstrate the superiority of our approach. For binary classification, BIOFUSE-DDI achieves 98.20% F1 score and 99.80% AUC, surpassing state-of-the-art methods by significant margins. In the more challenging multi-class prediction task, our model attains 96.01% macro-F1 and 97.55% mean-accuracy, establishing new benchmarks in DDI prediction. These results validate the effectiveness of integrating protein sequence features with knowledge graph information through our hierarchical fusion architecture.

External IDs:dblp:conf/icic/ZhaoCLL25