Abstract / Summary
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, and women are disproportionately affected. This review examines the evidence that estrogen signaling contributes to this disparity and asks what a molecularly targeted therapeutic would require. We first distinguish prevalence from incidence in the gender-difference literature and review the genetic, epigenetic, and ascertainment factors that structure female-specific risk, noting that the associations localize consistently to tau rather than to amyloid pathology. We then examine estrogen receptor (ER) biology in the brain, reconciling apparently contradictory accounts of ERα and ERβ function by resolving them at the level of downstream pathway and cell type rather than receptor identity, and review evidence that DNA methylation at ER genes predicts cognitive decline and tangle density through mechanisms independent of receptor expression. The consistent failure of systemic hormone therapy in clinical trials is interpreted not as a refutation of this biology but as a constraint defining three design requirements: receptor engagement resolved by subtype, cell type, and tissue compartment; sufficient central nervous system exposure; and delivery timed to the perimenopausal window. Against these requirements we review AI-driven approaches to ER modulator design, spanning diffusion-based backbone generation and protein language models for peptides, and virtual screening and generative frameworks for small molecules. We argue that the principal barrier is data rather than model architecture: ERβ-selective binding data and peptide blood–brain barrier permeability data are both scarce, and no AI-designed ER modulator has been evaluated in an AD model. We conclude by identifying the epigenetic layer as a target class that current design pipelines do not address, and for which the necessary editing tools are technically available but validated against the wrong readout.