AI-Driven Network Pharmacology of Traditional Chinese Medicine for Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) present escalating global health challenges with limited therapeutic options. Traditional Chinese Medicine (TCM), characterized by multi-component and multi-target approaches, offers promising complementary strategies. The integration of artificial intelligence (AI) with network pharmacology (NP) has revolutionized the understanding of TCM complex mechanisms through systematic analysis of compound-target-disease interactions. This review presents advances in AI-driven network pharmacology applications for elucidating TCM therapeutic mechanisms in major NDDs, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. We critically examine methodological frameworks, validation challenges, and clinical translation potential. Evidence demonstrates that AI-TCM-NP integration facilitates drug discovery and biomarker identification, though significant gaps remain between computational predictions and experimental validation. This analysis provides guidance for future research directions in applying computational approaches to traditional medicine for neurodegenerative disease therapy.