Mathematical Modeling of Aβ-42 Dimerization Dynamics: Integrating Physics-Based Simulations, Graph-Based Variational Autoencoder-Driven Neural Relational Inference, and Chaos Theory

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the pathological aggregation of amyloid-beta (Aβ) peptides, particularly Aβ-42, which plays a central role in disease progression. Soluble Aβ dimers have been implicated as the primary neurotoxic species contributing to synaptic dysfunction and cognitive impairment. In this study, we employ a comprehensive computational framework integrating molecular dynamics (MD) simulations, neural relational inference (NRI) modeling, and largest Lyapunov exponent (LLE) analysis to elucidate the molecular mechanisms underlying Aβ-42 dimerization and evaluate the inhibitory potential of small molecules, apigenin and caffeine. Our findings demonstrate that apigenin exhibits a stronger inhibitory effect on Aβ-42 aggregation compared to caffeine. MD simulations reveal that apigenin disrupts monomer–monomer interactions by destabilizing key aggregation-prone regions, particularly residues 29 and 30, as quantified by MM/GBSA binding-free energy calculations. The application of NRI modeling further confirms the role of apigenin in reducing residue–residue interaction strength, thereby preventing the formation of stable β-sheet structures. Additionally, LLE analysis highlights the ability of apigenin to mitigate chaotic fluctuations within Aβ-42 dynamics, stabilizing monomeric conformations while preventing dimerization. By integrating computational biophysics and mathematical modeling approaches, this study provides a novel mechanistic understanding of Aβ-42 aggregation and offers compelling evidence for apigenin as a promising therapeutic candidate for AD. These findings underscore the potential of natural small molecules in targeting early-stage Aβ-42 aggregation, paving the way for future experimental and clinical investigations.