Stroke is a complicated disease with an increasing incidence and a very high mortality rate. A classical Chinese herbal medicine, Dengzhan
Shengmai (DZSM), has shown to have
therapeutic effects on
stroke; however, its chemical basis and molecular mechanism are still unclear. In this study, a systems biology approach was applicable to elucidate the underlying mechanism of action of DZSM on
stroke. All the compounds were obtained from databases, and pendant-related targets were obtained from various data platforms, including the TCM Systematic Pharmacology (TCMSP) database, TCM Integrated Database (TCMIP), High Throughput Experimental Reference Database (HERB), Comparative Toxicogenomics Database (CTD), SwissTargetPredicition, and SymMap, The Human Gene Database (GENECARD) and Comparative Toxicogenomics Database (CTD) were used for
stroke disease target data, followed by network pharmacology analysis to predict the potential effect of DZSM on
stroke. Animal experiments were intended to validate the underlying mechanisms. A total of 846 chemical components were compiled for the targets of DZSM drug, and
quercetin,
kaempferol, and
Wuweizisu C are the highest chemical components compiled from DZSM. Overlapping with 375 disease-specific targets and 149 core targets, the core targets include TNF,
IL-6, ALB, and AKT1, which are shown to regulate the disease process from an anti-inflammatory perspective. 198 enrichment messages were obtained by KEGG enrichment analysis, and we believe that the role of the AGE-RAGE signaling pathway in
diabetic complications, TNF signaling pathway, and
IL-17 signaling pathway is more important. Based on rat experiments, we also demonstrated that DZSM could effectively modulate the
inflammation level of
brain infarct tissues and effectively alleviate behavioral characteristics. Grouped together, our study suggests that the combination of network pharmacology prediction and experimental validation can provide a useful tool to describe the molecular mechanisms of DZSM in Chinese medicine (TCM).