Novel graph attention autoencoder framework with multilayered validation identifies drug repurposing candidates for COVID-19 treatment

Background The COVID-19 pandemic highlighted the critical need for drug repurposing to rapidly identify therapeutic options. While computational graph-based approaches show promise, conventional single analytical methods often fail to capture the complex pathological mechanisms needed for clinical translation. Methods We developed a Graph Attention Autoencoder (GATE) framework with multilayered validation integrating computational prediction, real-world data, and phenome analyses. Network-based embeddings identified drug candidates through latent space similarity analysis within biomedical knowledge graphs. Clinical potential was assessed via disproportionality analysis (DPA) using adverse event reporting system, and biological plausibility was evaluated through gene expression profiling and pathway analyses. Results Our GATE framework identified 17 drug candidates, including 13 with known clinical effects on COVID-19 symptoms, validating model performance. Four novel candidates (cilastatin, megestrol, drotrecogin alfa, and ethacrynic acid) with minimal prior COVID-19 associations were identified. DPA further narrowed these to two promising candidates with significant protective signals: cilastatin (reporting odds ratio [ROR]: 0.19, 95% confidence interval [CI]: 0.09–0.43) and megestrol (ROR: 0.63, 95% CI: 0.42–0.94). Pathway profiling confirmed that both drugs share molecular signatures with drugs investigated in COVID-19 clinical trials. Gene expression analyses suggest that cilastatin may have anti-viral and anti-inflammatory effects via suppression of splicing, ribosomal function and mitochondrial pathway, through DPEP1 inhibition. Conclusions This study presents the first systematic integration of GATE-based computational prediction, real-world evidence from adverse event databases, and phenome-level pathway profiling for COVID-19 drug repurposing. This multilayered approach enabled multidimensional candidate validation unattainable by single analytical methods alone. The identification of clinically approved drugs with established safety profiles may facilitate accelerated clinical evaluation. Furthermore, the pathway and gene expression analyses provide mechanistic working hypotheses that can streamline subsequent preclinical validation. Although preclinical and clinical validation remain essential, this framework offers a generalizable strategy for rapid candidate identification during pandemics and for diseases with unmet therapeutic needs.