Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction
This work addresses the issue of misjudgment in drug discovery for scientists, though it appears incremental as it builds on existing knowledge graph embedding models.
The paper tackled the problem of inaccurate drug-target interaction predictions by introducing a causal intervention-based confidence measure to assess triplet scores, which significantly enhanced prediction accuracy, especially for high-precision models.
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.