Robust Machine Learning-Driven Drug Repositioning Pipeline for Classical Subtype of Pancreatic Ductal Adenocarcinoma

This study presents a novel machine learning-driven drug repositioning pipeline to identify potential drug candidates for the Classical Subtype of pancreatic ductal adenocarcinoma (PDAC). Our approach integrates the Graph Attention Neural Network version 2 (GATv2) model as the backbone to predict target genes of PDAC, achieving a high area under the curve (AUC) of 0.911. A robust two-step cross-validation workflow was also implemented to enhance model reliability and generalizability. Finally, PharmOmics network analysis was conducted for potential drug candidates' prediction. Through our pipeline, we successfully identified at least five promising drug candidates for PDAC, including Indomethacin, Sorafenib, Valproic Acid, Cyclosporine, and Irinotecan, showcasing the potential of our drug repositioning methodology. These findings demonstrated the effectiveness of machine learning in drug discovery and paved the way for further clinical exploration of these candidates for PDAC treatments.Clinical Relevance- This study leveraged machine learning for drug repositioning with robust modelling design. There were at least five promising drug candidates predicted through our pipeline. Our findings may assist clinicians in considering the proposed drug candidates to enhance treatment strategies and improve patient outcomes in PDAC.