Magnolol as a promising multitarget agent for Alzheimer's disease: Evidence from a systematic evidence synthesis, network pharmacology, and molecular dynamics simulations.

Abstract

Objective: This study provides systematic synthesis of therapeutic effects of magnolol (MN) in preclinical Alzheimer's disease (AD) models and integrates network pharmacology with molecular dynamics (MD) simulations to predict its core targets and binding stability.

Methods: Systematic literature search was conducted in PubMed, CNKI, Wanfang, and Google Scholar up to January 1, 2026 following PRISMA guidelines. Network pharmacology, molecular docking, and 100 ns MD simulations were used to identify common targets, evaluate binding affinities, and assess complex stability.

Results: Fourteen studies (seven in vivo, five in vitro, two combined) were included. MN consistently ameliorated cognitive deficits and neuropathology through antioxidant, anti-inflammatory, anti-apoptotic, and anti-acetylcholinesterase activities, while preserving mitochondrial and synaptic function. Network pharmacology identified 60 common targets; Protein-Protein Interaction (PPI) analysis revealed five hub genes: AKT1, MMP9, MMP2, ERBB2, and EGFR. Molecular docking showed binding energies below -4.0 kcal/mol for all five targets, and MD simulations confirmed stable binding, with the ERBB2-MN complex exhibiting the lowest root mean square deviation (RMSD) (1.2 Å) and favorable free energy landscape. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) calculations further confirmed that magnolol exhibited binding affinities comparable to the reference co‑crystal ligands. Unlike prior reviews, this study uniquely combines systematic evidence synthesis with computational predictions, identifying ERBB2 as a novel stable target of MN.

Conclusion: MN exerts anti-AD effects via multi-pathway and multi-target regulation, with computational predictions aligning with experimental evidence, supporting MN as promising lead compound for AD drug development.