KG-bench: benchmarking graph neural network algorithms for drug repurposing

Drug repurposing leverages existing drugs for new indications, accelerating drug development. Computational methods integrating diverse biological and chemical data can systematically prioritize repurposing candidates, but standardized benchmarks for deep learning evaluation are lacking. We present knowledge graph (KG)-Bench, a graph neural network (GNN) benchmarking framework designed to systematically compare the performance of different GNN architectures on drug-disease association prediction using the Open Targets dataset. We constructed a KG of drugs, diseases, and targets, including annotations such as therapeutic area and molecular pathway, and ensured retrospective validation by leveraging regular dataset updates. To avoid data leakage, we removed redundant entities across splits.

Benchmarking six GNN architectures, Relational Graph Convolutional Networks achieved the highest ranking performance (AUC: 0.91), while TransformerConv showed superior robustness under class imbalance (F1: 0.28 at 1:100 positive: negative ratio), characteristic of real drug repurposing datasets. KG-Bench also assesses bias, node/feature importance, and uses GNNExplainer for interpretability. Our open-source framework enables fair, reproducible evaluation of graph-based drug repurposing algorithms.

Data and codes are available at https://github.com/cmbi/Benchmark_GNN_OpenTargets.