Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation.

ABSTRACT:

Purpose

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. 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 feat

SUBMITTER: Lee T 

PROVIDER: S-EPMC12359948 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature