Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks
This work addresses the economically infeasible process of drug repurposing for pharmaceutical research, though it is incremental as it builds on existing methods with new data integration.
The study tackled drug repurposing by developing a machine learning pipeline using deep embedded clustering and graph neural networks to identify new drug-disease links from multi-omic data, achieving a prediction accuracy of 0.901 and generating 477 high-probability links.
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.