Abstract: A novel AI-driven drug-repurposing method, DeepDrug, is used to identify a lead combination of previously FDA-approved drugs to treat AD by targeting the upstream genetic markers along the AD pathology. A three-step methodology is used. First, a heterogeneous biomedical graph is constructed comprising complex and interconnected genes, proteins, and drug information to capture the network characteristics of the AD pathology, considering the expert known associations between different AD pathways and utilizing node weighting and edge weighting and direction. Second, the curated graph is taken as an input to an artificial intelligence (AI)-driven graphical neural network (GNN) framework, with embeddings of drug and gene nodes as the outputs. Third, a drug scoring and selection analysis is conduced to generate the drug-gene scores and identify a lead combination of repurposed AD drug candidates for clinical verification.