Bioinformatics-Driven Discovery of Aβ-Targeted Therapeutics and Diagnostic Biomarkers for Alzheimer's Disease

The deposition of amyloid-β (Aβ) plaque is widely recognized as one of the core pathological events of Alzheimer's disease (AD). This study focused on uncovering potential diagnostic biomarkers and small-molecule candidates potentially modulating Aβ-associated pathways in AD. Weighted gene coexpression network analysis (WGCNA) and machine learning identified the hub gene. Single-nucleus RNA sequencing (snRNA-seq) identified high-expression cell clusters. We identified potential Aβ-binding small-molecule candidates and calculated their binding affinities using molecular docking techniques. Based on molecular docking, we further evaluated the binding stability and conformational dynamics of the protein-ligand complex through molecular dynamics (MD) simulations. NFKBIA was identified as a hub diagnostic gene. Three small molecules, resveratrol, curcumin, and apigenin, demonstrated strong binding affinities for NFKBIA. These findings clarify the priority candidate gene and feasible strategic directions, which are expected to be translated into practical pathways for precision diagnosis and treatment of AD.