Network pharmacology and artificial intelligence in traditional Chinesemedicine for Alzheimer’s disease:Acomprehensive review

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, characterized by the accumulation of amyloid-beta, tau hyperphosphorylation, neuroinflammation, and oxidative stress. With current pharmacological treatments providing symptomatic relief, the need for other therapeutic approaches becomes evident. Traditional Chinese Medicine, with its multi-component and multi-target approach, offers promising potential for the management of AD, but the complex formulations have proved challenging to discern precise mechanisms of therapy. Network pharmacology, a systems biology approach, has emerged as a powerful tool in understanding the mechanisms of action of TCM by mapping bioactive compounds to AD-related pathways. This method enables the identification of synergistic interactions and key molecular targets, facilitating drug discovery and optimization. Furthermore, AI, particularly machine learning and deep learning algorithms, has revolutionized TCM research by analyzing large datasets, predicting compound-target interactions, and enabling personalized treatment approaches. AI-driven virtual screening and computational modeling have rapidly accelerated the identification of potential neuroprotective compounds, such as curcumin, ginsenosides, and huperzine A, which modulate multiple AD-associated pathways. The integration of network pharmacology and AI offers a systematic framework for validating TCM formulations and optimizing their therapeutic potential. This review highlights recent advancements in AI-assisted TCM research, discusses key bioactive compounds, and explores their mechanisms in AD treatment. While standardization and regulatory approval continue to be challenging, the synthesis of ancient knowledge with contemporary computing technologies holds enormous promise for effective, multi-target interventions for AD, thereby ushering in a new wave of innovative therapeutic approaches.