Compared with other models.

Drug-induced liver injury is a leading cause of high attrition rates for both candidate drugs and marketed medications. Previous in silico models may not effectively utilize biological drug property information and often lack robust model validation. In this study, we developed a graph convolutional network embedded with a biological graph learning (BioGL) module—named BioGL-GCN(Biological Graph Learning-Graph Convolutional Network)—for drug-induced liver injury prediction using toxicogenomic profiles. The BioGL module learned the optimal graph representations of gene interactions by utilizing the constructed protein-protein interaction network, which represents initial gene relationships, and gene frequency information obtained from gene enrichment analysis. Finally, the graph convolutional network was used to identify drug hepatotoxicity. Our method pays more attention to gene-gene relationships compared to previous approaches, thereby achieving more accurate predictive performance. We applied BioGL-GCN to predict DILI risk for active components in the integrated traditional Chinese medicine (ITCM) database and validated these predictions through hepatotoxicity experiments using a 3D primary human hepatocyte (PHH) model. The results showed that our model achieved a prediction accuracy of 79%, thus further validating the reliability of the constructed model.