Deep contrastive learning model boosts cell-specific Alzheimer's drug target discovery by 18-24% AUC

In silico, deep contrastive learning model alzCL boosted Alzheimer's disease-associated gene detection by 18-24% AUC versus prior methods, identifying cell-type-specific targets and potential drugs like selonsertib, but lacks clinical validation.

• Computational framework integrated human brain single-nucleus RNA-seq with protein-protein interactome; no direct patient cohort.
• Model identified 16, 164, and 221 AD-associated genes in astrocytes, microglia, and inhibitory neurons, respectively; top genes enriched in inflammatory pathways.
• Purely computational study; no external or clinical validation of identified drug targets.

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