DTRE: A model for predicting drug-target interactions of endometrial cancer based on heterogeneous graph

Abstract

Endometrial cancer is one of the most common gynecological malignancies affecting women worldwide, posing a serious threat to women’s health. Moreover, the identification of drug-target interactions (DTIs) is typically a time-consuming and costly critical step in drug discovery. In order to identify potential DTIs to enhance targeted therapy for endometrial cancer, we propose a deep learning model named DTRE (Drug-Target Relationship Enhanced) based on a heterogeneous graph to predict DTIs, which utilizes the relationships between drugs and targets to effectively capture their interactions. In the heterogeneous graph, nodes represent drugs and targets, and edges represent their interactions, then the representations of drugs and targets are learned through graph convolutional network, graph attention network and attention mechanism. Experimental results on the dataset proposed in this paper show that the AUC and AUPR of DTRE achieve 0.870 and 0.872 respectively, significantly outperforming comparative models and indicating that DTRE can effectively predict DTIs when applied to large-scale data. Additionally, DTRE also predicts the potential DTIs for endometrial cancer, providing new insights into targeted therapy for it.