Enhanced drug-disease association prediction through representation learning on similarity networks.

Abstract

Drug repositioning has emerged as a promising strategy for accelerating therapeutic discovery by identifying novel indications for existing drugs. Recent graph representation learning methods have shown encouraging performance for drug-disease association prediction; however, many existing approaches directly utilize heterogeneous drug-disease networks during representation learning, potentially introducing label leakage and limiting generalizability. In this study, we propose similarity network-based representation learning for drug repositioning (SimNetRLDR), a similarity network-based representation learning framework for drug repositioning. The proposed method independently learns drug and disease embeddings from homogeneous similarity networks using a weighted graph attention network encoder. The learned representations are subsequently integrated and used for downstream drug-disease association prediction through an extreme gradient boosting (XGBoost) classifier. Comprehensive experiments were conducted on benchmark datasets under single and integrated/multiplex disease similarity network settings. SimNetRLDR consistently outperformed existing methods, achieving superior area under the receiver operating characteristic curve, area under the precision-recall curve, F1-score, and accuracy with strong robustness across cross-validation folds. Additional robustness evaluations using external dataset, drug-wise and disease-wise cold-start settings further demonstrated the generalizability of the proposed framework, particularly for unseen drugs. Hyperparameter sensitivity analysis demonstrated stable performance across different neighborhood sizes and attention head numbers. Component-wise ablation studies further confirmed the effectiveness of the weighted graph attention encoder and the decoupled XGBoost classifier design. To evaluate biological and clinical relevance, we analyzed predicted associations supported by shared Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and manually curated evidence from ClinicalTrials.gov. After rigorous evidence filtering, 12 predicted drug-disease associations showed plausible clinical support, including Sulindac-Breast Neoplasms, Methotrexate-Schizophrenia, and Liothyronine-Breast Neoplasms. Overall, these findings demonstrate that SimNetRLDR provides an effective, robust, and biologically meaningful framework for computational drug repositioning.