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Background <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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FSSM-DDI: Fusion State Space Model for predicting drug-drug interaction using social-media and drug descriptions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • Shanwen Zhang,
  • Ting Zhang,
  • Dengwu Wang,
  • Xiaohua Zhang

摘要

Background

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.

Results

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.

Conclusions

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.