Improving Drug-Induced Liver Injury Prediction Using Graph Neural Networks with Augmented Graph Features from Molecular Optimisation

BACKGROUND. Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to the discontinuation of clinical trials and the withdrawal of drugs from the market due to severe hepatotoxicity. This study explores the application of graph neural networks (GNNs) for DILI prediction, using molecular graph representations as the primary input. METHODS. We evaluated several GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and Aggregation (GraphSAGE), and Graph Isomorphism Networks (GINs), using the latest FDA DILI dataset and other molecular property prediction datasets. We introduce a novel approach that creates a custom graph dataset, driven by molecular optimisation, that incorporates detailed and realistic chemical features such as bond lengths and partial charges as input into the GNN models. We have named our model approach DILIGeNN. RESULTS. DILIGeNN achieved an AUC of 0.897 on the DILI dataset, surpassing current state-of-the-art model in the DILI prediction task. Furthermore, DILIGeNN outperformed the state-of-the-art in other graph-based molecular prediction tasks, achieving an AUC of 0.918 on the Clintox dataset and 0.993 on the BBBP dataset and 0.953 on the BACE dataset, indicating strong generalisation and performance across different datasets. CONCLUSION. DILIGeNN, without relying on biological data, outperforms the state-of-the-art methods in DILI prediction that incorporate both chemical and biological data. These findings highlight the effectiveness of our molecular graph generation and the GNN training approach as a powerful tool for early-stage drug development and drug repurposing pipeline.