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.

The authors have declared no competing interest.

This research was supported in part by the US National Academy of Medicine (NAM) Healthy Longevity Catalyst Award 2021 and "An Artificial Intelligence (AI)-driven Causal Approach for Early Diagnosis and Treatment of Late Onset Alzheimer's Disease" under Seed Fund for Collaborative Research, The University of Hong Kong, June 2023.

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The drug and drug-drug interaction data were obtained from DrugBank (https://go.drugbank.com). The drug target data were obtained from DrugBank (https://go.drugbank.com), DrugCentral (https://drugcentral.org), ChEMBL (https://www.ebi.ac.uk/chembl/), and BindingDB (https://www.bindingdb.org/). The protein-protein interaction and pathway data were obtained from STRING (https://string-db.org). The preprocessed biomedical network datasets, including genes and their weights (Table S1), drugs and their weights (Table S2), proteins/targets and their weights (Table S3), and edges and their weights (Tables S4-7), are available in Supplementary Data. The UK Biobank data used in this study can be accessed by qualified researchers through formal application at https://www.ukbiobank.ac.uk, subject to approval and compliance with ethical guidelines.

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The drug and drug-drug interaction data were obtained from DrugBank (https://go.drugbank.com). The drug target data were obtained from DrugBank (https://go.drugbank.com), DrugCentral (https://drugcentral.org), ChEMBL (https://www.ebi.ac.uk/chembl/), and BindingDB (https://www.bindingdb.org/). The protein-protein interaction and pathway data were obtained from STRING (https://string-db.org). The preprocessed biomedical network datasets, including genes and their weights (Table S1), drugs and their weights (Table S2), proteins/targets and their weights (Table S3), and edges and their weights (Tables S4-7), are available in Supplementary Data. The UK Biobank data used in this study can be accessed by qualified researchers through formal application at https://www.ukbiobank.ac.uk, subject to approval and compliance with ethical guidelines.