Active Learning‐Assisted Exploration of [PO ₄₀ Mo ₁₂ ] ³⁻ for Alzheimer's Therapy Insights

Abstract Alzheimer's disease (AD), involving amyloid‐β (Aβ) aggregation, has potential therapeutic modulators in polyoxometalates (POMs) like [PMo ₁₂ O ₄₀ ] ³⁻ . To clarify their inhibitory mechanisms, a multiscale computational strategy integrating active‐learning Bayesian Optimization (BO) and density functional theory (DFT) is employed to explore low‐energy configurations of isolated amino acids, [PMo ₁₂ O ₄₀ ] ^{3 −} –amino acid complexes, and [PMo ₁₂ O ₄₀ ] ^{3 −} –peptide systems. Hydrogen bonding and Coulombic repulsion dominate adsorption stability. Crucially, oxygen atoms in the [PMo ₁₂ O ₄₀ ] ^{3 −} cluster form multiple weak interactions (e.g., van der Waals, hydrophobic) with alkyl side‐chain hydrogens in Aβ peptides. The synergistic effect of these weak interactions induces robust binding between the POM and peptide chains, stabilizing a tightly bound complex that sterically hinders Aβ self‐assembly. Notably, simulations predict that the cluster preferentially targets hydrophobic amino acids with alkyl chains (valine, lysine, leucine, isoleucine) located in Aβ regions critical for aggregation—specifically, namely Aβ12, Aβ16‐18, Aβ24, Aβ28, Aβ31‐32, Aβ34‐36, and Aβ39‐41. These insights highlight the role of multivalent weak interactions in POM‐mediated inhibition and identify key interfacial residues for therapeutic targeting.