Background:
Parkinson’s disease (PD) is a progressive neurodegenerative disorder with loss of nigrostriatal dopaminergic neurons. Epidemiological data link habitual caffeine intake to lower PD risk, but molecular mechanisms remain unclear. We applied network pharmacology to test whether predicted caffeine targets converge on host pathways relevant to PD. Methods: Predicted caffeine targets were retrieved from SwissTargetPrediction (cross-checked with DrugBank) and intersected with PD-associated genes from the Open Targets Platform. Overlap analysis identified ۹۰ shared proteins. A protein–protein interaction network was constructed in STRING and analyzed with Cytoscape. Topological and centrality metrics (NetworkAnalyzer) and GO enrichment (BP/CC/MF) with multiple-testing correction identified enriched themes. Results: The caffeine–PD interactome comprised ۹۰ proteins and exhibited features of a moderately connected functional network. In the analyzed ۲۳-node subset, the average node degree was ۸.۲۶ (median = ۸; range = ۳–۱۹) and the mean betweenness centrality was ۰.۰۳۴۱. Hub analysis identified CASP۳ as the most central node (degree = ۱۹; betweenness = ۰.۲۸۷), followed by MAOB, EGFR, PSEN۱, ACE, and ACHE. GO enrichment analysis revealed convergence on several biological processes relevant to Parkinson’s disease, including neuronal signaling, mitochondrial function, proteostasis, and cellular stress and apoptotic regulation. Conclusion:
Network pharmacology indicates caffeine’s predicted targets intersect multiple PD-relevant pathways—synaptic modulation, mitochondrial function, proteostasis, and oxidative/apoptotic control—and highlights hub proteins (CASP۳, MAOB, EGFR, PSEN۱, ACE, ACHE) as priority candidates for experimental validation. Follow-up should extract leading-edge genes, validate hubs in cellular/animal PD models, and address pharmacokinetics and BBB penetrance.