  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.
