Allicin-Flavonoid Combination Therapies for Alzheimer's Disease Using a Machine Learning and Computational Modeling Analysis

The treatment of Alzheimer's disease (AD) requires multiple therapeutic targets to address its complex disease mechanisms. The research evaluates how allicin from garlic and various flavonoids work together to combat the major mechanisms of Alzheimer's disease, which include amyloid-beta (Aβ) aggregation, tau hyperphosphorylation, neuroinflammation, and oxidative stress. The experimental assessment of allicin-flavonoid pairs through screening methods proves too costly and time-consuming, so we created a predictive machine learning (ML) system to identify the effective combinations of metrics for neuroprotection. The research combined Density Functional Theory (DFT) calculations with Quantitative Structure-Activity Relationship (QSAR) modeling for our methodology. The B3LYP/6-311+G(d,p) method enabled us to determine exact Bond Dissociation Enthalpy (ΔBDE) changes, which measured how flavonoids enhance allicin's antioxidant properties when they form complexes. The Topological Polar Surface Area (TPSA) parameter was used to determine the degree of molecular polarity in a compound. MolWt (Molecular Weight) and AlogP were employed to measure the size and the lipophilicity of a substance, which indicates its ability to dissolve in biofluids. The Random Forest Regressor model processed ΔBDE data to achieve outstanding predictive performance (R² = 0.89) while identifying nOH and TPSA as essential molecular descriptors for radical stabilization. The ML-QSAR framework enables us to develop new nutraceutical and nanotherapeutic agents through rational design methods, which allow them to screen flavonoid libraries at high speed to select candidates for experimental testing in AD models, thus speeding up the creation of effective combination therapies for Alzheimer's disease progression.