Drug-Drug Interaction (DDI) extraction is the task of identifying drug entities and the potential interactions between drug pairs from biomedical literature. Computer-aided extraction of DDIs is vital for drug discovery, as this process remains extremely expensive and time consuming. Therefore, Machine Learning-based approaches can reduce the laborious task during the drug development cycle. Numerous traditional and Neural Network-based approaches for Drug Named Entity Recognition (DNER) and the classification of DDIs have been proposed over the years. However, despite the development of many effective methods, achieving good prediction accuracy is an area where significant improvement can be made. In this article, we present a novel end-to-end approach that tackles the overall DDI extraction task as a pipelined method via the Transformer model architecture and biomedical domain pre-trained weights. In our approach, the tasks of DNER and DDI classification are executed successively to extract the drug entities and to classify their relationship respectively. The proposed approach, TP-DDI, integrates prior knowledge by using pre-trained weights from BioBERT and improves in both the Drug Named Entity Recognition and the overall DDI extraction task over the current state-of-the-art approaches on the DDI Extraction 2013 corpus.

药物相互作用 (DDI) 提取是从生物医学文献中识别药物实体和药物对之间潜在相互作用的任务。DDI 的计算机辅助提取对于药物发现至关重要，因为这个过程仍然非常昂贵和耗时。因此，基于机器学习的方法可以减少药物开发周期中的繁重任务。多年来，已经提出了许多用于药物命名实体识别 (DNER) 和 DDI 分类的传统和基于神经网络的方法。然而，尽管开发了许多有效的方法，但实现良好的预测精度是一个可以进行重大改进的领域。在本文中，我们提出了一种新颖的端到端方法，通过 Transformer 模型架构和生物医学领域预训练权重，将整体 DDI 提取任务作为流水线方法处理。在我们的方法中，连续执行 DNER 和 DDI 分类任务以提取药物实体并分别对它们的关系进行分类。所提出的方法 TP-DDI 通过使用来自 BioBERT 的预训练权重整合了先验知识，并在 DDI Extraction 2013 上的当前最先进方法的基础上改进了药物命名实体识别和整体 DDI 提取任务语料库。