OBJECTIVE:To decipher the antidepressant targets and mechanisms of Huangqin(Radix Scutellariae Baicalensis)(RSB) by a novel computational system based on prediction and experimental verification.METHODS:The putative targets of RSB against depression were identified from Traditional Chinese Medicine Systems Pharmacology(TCMSP) and Drug Bank. Next, protein-protein interaction network of the anti-depression targets of RSB were identified, and differentially expressed genes(DEGs) of depression were mined from the NCBI database. Then, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology were used to analysis the common targets. Finally, the selected pathways and functions were verified by experimentation.RESULTS:Thirty active compounds in RSB were predicted with high confidence by TCMSP and DrugBank, and seventy-one DEGs were identified in the GEO database. Besides, eight core target proteins were screened out by descending order of degree value, including ACHE, IL6, SLC6A4, FOS, SLC6A3,MAOB, DPP4, and JUN. These target genes were further found to be associated with pathways involved in neuronal apoptosis, such as pathways in cancer, Toll-like receptor signaling pathway, and TNF signaling. The cell proliferation assay and wound-healing assay results showed that RSB does not affect PC12 cell proliferation and chemotaxis. Unexpectedly, RSB protected PC12 cells from oxidative stress induced by H_2O_2 via inhibiting autophagy and apoptosis. We revealed significant changes in mice treated with 400 mg/kg RSB compared with the lipopolysaccharide mice. The possible mechanism for the antidepressive action of RSB is by reducing the expression of LC3-B in CA1 neurons.CONCLUSIONS:Our research partially expounds the mechanism of the antidepressant effect of RSB by the combination of network pharmacology prediction and experimental verification. Furthermore,it is also conducive to the application of Traditional Chinese Medicine within modern medicine.