Predicting drug-drug interactions (DDIs) from social-media and drug descriptions is crucial for healthcare, drug regulation, and pharmaceutical research, yet remains a challenging task. Existing deep learning and Transformer-based approaches often struggle with modeling long-range dependencies within sentences or entail high computational costs, limiting their practical applicability in large-scale drug safety screening. To overcome these limitations, we propose a Fusion State Space Model (FSSM) for DDI prediction (DDIP). FSSM leverages a selective State Space Model (SSM) to efficiently capture long-range syntactic and semantic dependencies within sentences, while an Interaction-based Selective Filtering (ISF) module mitigates information redundancy from multimodal inputs. Experiments on the DDIExtraction-2013 corpus demonstrate that FSSM achieves an F1-score of 75.23%, matching state-of-the-art Transformer models while requiring significantly less training time. Ablation studies confirm that each architectural component contributes meaningfully, with social-media embedding providing the largest performance gain (8.93% drop upon removal) and the ISF module contributing a 4.80% improvement. FSSM offers a promising balance of predictive performance and computational efficiency for drug-drug interaction prediction. The model’s linear complexity enables large-scale screening, real-time applications, and deployment in resource-constrained settings, making it a valuable tool for drug discovery and drug safety assessment.
Zhang et al. (Mon,) studied this question.