School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, China.
NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangzhou, China.
PLoS Comput Biol. 2024 Apr 16;20(4):e1011989. doi: 10.1371/journal.pcbi.1011989. eCollection 2024 Apr.
Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts.
生物医学文本为药物-药物相互作用（DDI）在药物警戒领域的研究提供了重要数据。尽管研究人员已经尝试从生物医学文本中挖掘药物相互作用并预测未知的药物相互作用，但缺乏准确的人工注释极大地阻碍了机器学习算法的性能。在这项研究中，开发了一种新的药物相互作用预测框架，即基于子图增强模型的药物相互作用预测（SubGE-DDI），以提高机器学习算法的性能。该模型使用药物对知识子图信息来实现大规模的纯文本预测，而无需大量注释。该模型将药物相互作用预测视为多类分类问题，并预测每个药物对的特定药物相互作用类型（例如机制、效果、建议、相互作用和负面）。药物对知识子图源自包含各种公共数据集的庞大药物知识图谱，例如 DrugBank、TwoSIDES、OffSIDES、DrugCentral、EntrezeGene、SMPDB（小分子途径数据库）、CTD（比较毒理学基因组数据库）和 SIDER。SubGE-DDI 从公共数据集（SemEval-2013 Task 9 数据集）进行评估，然后与其他最先进的基线进行比较。SubGE-DDI 在测试数据集中的微 F1 得分和宏 F1 得分分别为 83.91%和 84.75%，优于其他最先进的基线。这些发现表明，所提出的基于药物对子图辅助模型的方法可以有效地提高从生物医学文本中预测药物相互作用的性能。
