KG-Bench: Benchmarking Graph Neural Network Algorithms for Drug Repurposing

Abstract

MotivationDrug 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 KG-Bench, a benchmarking framework for drug-disease association prediction using the Open Targets dataset. We constructed a knowledge graph (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. ResultsBenchmarking several graph neural network (GNN) architectures, TransformerConv achieved the highest performance (APR: 0.87). 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. Availability and ImplementationData and codes are available at https://github.com/cmbi/Benchmark_GNN_OpenTargets.