Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks

• URL: http://arxiv.org/abs/2509.11493v1
• Date: Mon, 15 Sep 2025 01:04:37 GMT
• Title: Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks
• Authors: Luke Delzer, Robert Kroleski, Ali K. AlShami, Jugal Kalita,
• Abstract summary: We propose a machine learning pipeline that uses unsupervised deep embedded clustering, combined with supervised graph neural network link prediction.<n>A total of 9,022 unique drugs were partitioned into 35 clusters with a mean silhouette score of 0.8550.<n>Graph neural networks achieved strong statistical performance, with a prediction accuracy of 0.901, receiver operating characteristic area under the curve of 0.960, and F1-Score of 0.901.
• Score: 10.858347895657737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drug repurposing has historically been an economically infeasible process for identifying novel uses for abandoned drugs. Modern machine learning has enabled the identification of complex biochemical intricacies in candidate drugs; however, many studies rely on simplified datasets with known drug-disease similarities. We propose a machine learning pipeline that uses unsupervised deep embedded clustering, combined with supervised graph neural network link prediction to identify new drug-disease links from multi-omic data. Unsupervised autoencoder and cluster training reduced the dimensionality of omic data into a compressed latent embedding. A total of 9,022 unique drugs were partitioned into 35 clusters with a mean silhouette score of 0.8550. Graph neural networks achieved strong statistical performance, with a prediction accuracy of 0.901, receiver operating characteristic area under the curve of 0.960, and F1-Score of 0.901. A ranked list comprised of 477 per-cluster link probabilities exceeding 99 percent was generated. This study could provide new drug-disease link prospects across unrelated disease domains, while advancing the understanding of machine learning in drug repurposing studies.

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