Yang Bin, Song Dan, Li Yadong, Wang Jinglong
School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China.
College of Food Science and Pharmaceutical Engineering, Zaozhuang University, Zaozhuang, 277160, China.
Sci Rep. 2025 May 28;15(1):18635. doi: 10.1038/s41598-025-00725-9.
Predicting drug-drug interactions (DDI) is crucial for preventing adverse reactions in patients and plays a vital role in drug design and development. However, traditional Chinese medicine (TCM) formulations, typically composed of multiple herbal ingredients with diverse bioactive compounds, present a unique challenge in comprehensively assessing potential adverse interactions among their components. To address this challenge, we propose a novel Dual Graph Attention Network (DGAT) designed to predict TCM drug-drug interactions (TCMDDI) by extracting key structural features of active molecules within the herbal ingredients. Our approach leverages graph-based representations of chemical molecules and employs attention mechanism to extract deep structural features, enabling the effective prediction of TCMDDI by capturing spatial structural relationships among different compounds. Furthermore, we construct a comprehensive dataset encompassing three different categories of herbal ingredients, informed by traditional TCM principles. Experimental results reveal that the proposed DGAT method significantly outperforms currently advanced deep learning techniques, including Graph Convolutional Networks, Weave, and Message Passing Neural Networks. Compared to traditional rule-based two-dimensional molecular descriptors, DGAT more effectively captures the spatial structural information of molecules. Notably, DGAT exhibits robust performance and strong generalizability on unseen samples, providing valuable insights for future research on TCMDDI prediction and advancing the integration of artificial intelligence in TCM studies.
预测药物相互作用（DDI）对于预防患者的不良反应至关重要，并且在药物设计和开发中发挥着关键作用。然而，中药配方通常由多种含有不同生物活性化合物的草药成分组成，在全面评估其成分之间潜在的不良相互作用方面提出了独特的挑战。为应对这一挑战，我们提出了一种新颖的双图注意力网络（DGAT），旨在通过提取草药成分中活性分子的关键结构特征来预测中药药物相互作用（TCMDDI）。我们的方法利用化学分子的基于图的表示，并采用注意力机制来提取深度结构特征，通过捕捉不同化合物之间的空间结构关系，实现对TCMDDI的有效预测。此外，我们根据传统中医原则构建了一个包含三类不同草药成分的综合数据集。实验结果表明，所提出的DGAT方法显著优于当前先进的深度学习技术，包括图卷积网络、Weave和消息传递神经网络。与传统的基于规则的二维分子描述符相比，DGAT能更有效地捕捉分子的空间结构信息。值得注意的是，DGAT在未见样本上表现出强大的性能和很强的泛化能力，为未来中药药物相互作用预测研究提供了有价值的见解，并推动了人工智能在中医研究中的整合。
