Alzheimer's disease is a progressive neurodegenerative disorder characterized by hyperphosphorylation of the microtubule-associated protein tau, leading to neurofibrillary tangle formation and neurodegeneration. Multiple kinases contribute to tau phosphorylation, making them potential targets for therapeutic strategies aimed at altering disease progression. Identifying selective kinase inhibitors with favourable pharmacokinetic properties and sufficient brain penetration remains challenging. Thirteen kinases were prioritized based on predicted MAPT serine phosphorylation sites, using a percentile-based filtering strategy. For each kinase, a set of known inhibitors was selected and subjected to docking analysis using Boltz-2, a computational framework for predicting protein-ligand interactions. Predicted binding affinity likelihood scores and IC₅₀-like affinity values were integrated into composite scores to enable relative comparison of inhibitors within each kinase-specific set.