A network analysis framework for traditional Chinese medicine in treating Alzheimer’s disease: From core herbal pairs to key component networks

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder for which Traditional Chinese Medicine (TCM) has shown potential therapeutic advantages. This study aimed to explore the medication patterns and core herbal pairs used in the treatment of AD by integrating data mining, network pharmacology, and molecular docking techniques. Relevant literature on TCM interventions for AD was retrieved from five major databases, and core herbal pairs were identified. To further investigate the multi-target and multi-pathway mechanisms of these pairs and their key components, a comprehensive network was constructed. A core herbal pair—Acori Tatarinowii Rhizoma-Polygalae Radix-was selected, and its component–target–pathway network was established. GO and KEGG enrichment analyses were conducted to elucidate biological functions and signaling pathways, while molecular docking was performed to validate the interactions between key compounds and their targets. A total of 482 TCM formulas for AD were analyzed, identifying Acori Tatarinowii Rhizoma-Polygalae Radix as the most representative core pair. Its major active components, including phenylpropanoids (e.g., caffeic acid, β-asarone) and saponins (e.g., tenuigenin), were found to modulate inflammation, apoptosis, and synaptic plasticity through critical pathways such as MAPK and PI3K-Akt. Molecular docking demonstrated strong binding affinities between these key components and pivotal targets such as AKT1 and MAPK3. This study establishes a multi-layered framework, from big data analysis to functional validation of specific components, offering a clear pathway for investigating the structure-effect relationships in TCM. Furthermore, it proposes an innovative computational approach for analyzing the complex mechanisms of TCM, providing new insights into optimizing TCM formulations and developing novel drugs for AD.