DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Abstract

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025) relied on somatic mutation data and emphasized long genes to guide AD drug repurposing. However, emerging evidence suggests that germline genetic variants play a more central role in AD pathogenesis. In response, we develop DeepDrug2, an enhanced AI-driven framework for AD drug repurposing centered on germline mutations and validated using real-world electronic health records (EHRs). DeepDrug2 introduces four major innovations. First, it proposes a different hypothesis prioritizing germline over somatic mutations in influencing AD risk. Second, it updates the signed directed heterogeneous biomedical graph by removing somatic mutations, long genes, and expert-led genes from the previous version, and incorporating new genes identified in recent genome-wide association study (GWAS) findings. Third, it generates a new list of drug candidates by encoding this updated graph into a new embedding space via a graph neural network (GNN) and calculating drug-gene scores. Fourth, it performs real-world clinical validation using EHR data from over 500,000 individuals (including more than 4,000 AD cases) in the UK Biobank, evaluating associations between drug usage and AD onset while controlling for demographic and comorbidity factors. DeepDrug2 has identified several promising drug candidates. Among the top 15 candidates with sufficient medication records to support statistically powered analysis, Amlodipine (a calcium channel blocker), Indapamide (a thiazide-like diuretic), and Atorvastatin (a statin) were significantly associated with reduced AD risk ( p < 0.05). These findings highlight the role of germline mutations in guiding AD drug repurposing and emphasize the value of integrating real-world clinical data into AI-driven drug discovery. To further validate these candidates, future work will involve experimental studies using mouse and zebrafish models of AD. DeepDrug2 offers a promising strategy to support future clinical studies and expand therapeutic options for AD. Future work will evolve DeepDrug2 into a more powerful, versatile, and precise tool for AI-driven drug repurposing in neurodegenerative diseases by deeply integrating advanced LLM capabilities, prioritizing critical disease mechanisms, including tau pathology, and holistically incorporating multi-modal data sources.