Deep Learning-Based Prediction of Drug-Induced Liver Injury Using Molecular Graph Representations
Authors: ZHANG Wei, LI Ming, WANG Fang, CHEN Yu
Drug-induced liver injury (DILI) is a major cause of acute liver failure and a leading reason for drug attrition during development. Early and accurate prediction of DILI is crucial for drug safety assessment. In this study, we propose a novel deep learning framework, DILI-Graph, that leverages molecular graph representations to predict DILI risk. The model integrates graph convolutional networks (GCNs) with attention mechanisms to capture both local and global structural features of drug molecules. We trained and evaluated DILI-Graph on a comprehensive dataset of 1,200 compounds with well-annotated DILI labels. Our model achieved an area under the receiver operating characteristic curve (AUC) of 0.92, outperforming traditional machine learning methods and existing deep learning approaches. Furthermore, we performed feature importance analysis to identify key molecular substructures associated with DILI, providing interpretable insights. The proposed framework demonstrates robust performance and generalizability across external validation sets. Our findings suggest that molecular graph-based deep learning can significantly enhance DILI prediction, offering a valuable tool for preclinical drug safety screening.