Luo Yue, Deng Lei, Huang Zhijian
School of Computer Science and Engineering, Central South University, Changsha, China.
BMC Bioinformatics. 2025 Jun 4;26(1):152. doi: 10.1186/s12859-025-06157-6.
Accurate identification of drug-drug interactions (DDIs) is critical in pharmacology, as DDIs can either enhance therapeutic efficacy or trigger adverse reactions when multiple medications are administered concurrently. Traditional methods for identifying DDIs are labor-intensive and time-consuming, prompting the development of computational alternatives. However, existing computational approaches frequently encounter challenges related to interpretability and struggle to effectively capture the complex, multi-level structures inherent in drug molecules. Specifically, they often fail to adequately analyze substructural components and neglect interactions across hierarchical structural levels, resulting in incomplete molecular representations.
In this study, we propose a Hierarchical Learning Network with a co-attention mechanism tailored to molecular structure representation for predicting DDIs, named HLN-DDI. The proposed method advances existing approaches by explicitly encoding motif-level structures and capturing hierarchical molecular representations at atom-level, motif-level, and whole-molecule scales. These hierarchical representations are integrated using a co-attention mechanism and combined with interaction-type information to enhance predictive performance. Comprehensive evaluations demonstrate that HLN-DDI significantly outperforms state-of-the-art methods across multiple benchmark datasets, achieving over 98% accuracy under transductive scenarios and surpassing 99% on various evaluation metrics. Moreover, HLN-DDI achieves a notable accuracy improvement of 2.75% in predicting DDIs involving unseen drugs. Practical assessments with real-world DDI scenarios further validate the efficacy and utility of our proposed model.
By leveraging hierarchical molecular structures and employing a co-attention mechanism to effectively integrate multi-level representations, HLN-DDI generates comprehensive and precise drug representations, leading to substantially improved predictions of potential drug-drug interactions.
准确识别药物相互作用（DDIs）在药理学中至关重要，因为当同时使用多种药物时，药物相互作用既可以增强治疗效果，也可能引发不良反应。传统的药物相互作用识别方法需要耗费大量人力和时间，这促使了计算方法的发展。然而，现有的计算方法在可解释性方面经常遇到挑战，并且难以有效捕捉药物分子中固有的复杂多层次结构。具体而言，它们往往无法充分分析子结构成分，忽视跨层次结构水平的相互作用，导致分子表示不完整。
在本研究中，我们提出了一种具有协同注意力机制的分层学习网络，专门用于预测药物相互作用的分子结构表示，名为HLN-DDI。该方法通过显式编码基序级结构并在原子级、基序级和全分子尺度上捕捉分层分子表示，改进了现有方法。这些分层表示通过协同注意力机制进行整合，并与相互作用类型信息相结合，以提高预测性能。综合评估表明，HLN-DDI在多个基准数据集上显著优于现有方法，在转导场景下准确率超过98%，在各种评估指标上超过99%。此外，HLN-DDI在预测涉及未见过药物的药物相互作用时，准确率显著提高了2.75%。在实际药物相互作用场景中的实际评估进一步验证了我们提出模型的有效性和实用性。
通过利用分层分子结构并采用协同注意力机制有效整合多层次表示，HLN-DDI生成了全面而精确的药物表示，从而显著改进了对潜在药物相互作用的预测。
