Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently, knowledge graph and knowledge graph embedding (KGE) models have made rapid advancements and demonstrated impressive performance in drug discovery. However, such models lack authenticity and accuracy in drug target identification, leading to an increased misjudgment rate and reduced drug development efficiency. To address these issues, we focus on the problem of drug-target interactions, with knowledge mapping as the core technology. Specifically, a causal intervention-based confidence measure is employed to assess the triplet score to improve the accuracy of the drug-target interaction prediction model. Experimental results demonstrate that the developed confidence measurement method based on causal intervention can significantly enhance the accuracy of DTI link prediction, particularly for high-precision models. The predicted results are more valuable in guiding the design and development of subsequent drug development experiments, thereby significantly improving the efficiency of drug development.

翻译：识别和发现药物-靶点相互作用（DTIs）是药物研发过程中的关键步骤，在协助科学家发现新药、加速药物开发流程方面发挥着重要作用。近年来，知识图谱与知识图谱嵌入（KGE）模型取得快速进展，并在药物发现领域展现出卓越性能。然而，此类模型在药物靶点识别中存在真实性与准确性不足的问题，导致误判率上升、药物开发效率降低。针对上述问题，本研究聚焦药物-靶点相互作用问题，以知识图谱构建为核心技术。具体而言，采用基于因果干预的置信度测量方法评估三元组得分，以提升药物-靶点相互作用预测模型的准确性。实验结果表明，所开发的基于因果干预的置信度测量方法能显著增强DTI链接预测精度，尤其在高精度模型中表现突出。预测结果对后续药物开发实验的设计与开展具有更具价值的指导意义，从而显著提升药物研发效率。