Abstract: Objective: To investigate the effects of Saikosaponin A (SSA) on diabetic nephropathy (DN) and its possible molecular mechanisms based on in vitro cell experiments, network pharmacology and molecular docking. Methods: Rat renal tubular epithelial cells (NRK-52E) were used as the research subject. The cytotoxicity of SSA was determined by MTT assay, and the safe concentration of SSA was screened and its effects on the level of reactive oxygen species (ROS) induced by high glucose (HG) was detected. Western blotting was used to analyze the effects of SSA on the expression of FN protein. In network pharmacology part, SSA targets were screened based on Swiss Target Prediction platform, UniProt and TCMSP databases. Through the combination of OMIM database and GeneCards database, the targets of DN were screened. Venn diagram was used to obtain the intersection targets of the two, and STRING database was used to construct a protein-protein interaction (PPI) analysis network. Cytoscape 3.9.1 software was used for visual analysis to screen the core targets. The DAVID platform was used for GO functional enrichment analysis and KEGG enrichment analysis, and Cytoscape 3.9.1 software was used to construct the SSA-key targets-pathways-DN network. CB-Dock2 was used to complete drug-target molecular docking. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to verify the possible targets of SSA. Results: The results showed that SSA≤8 μmol/L had no obvious toxic effects on NRK-52E cells (P＞0.05). SSA at 0.5 μmol/L had the most significant inhibitory effects on HG-induced ROS overproduction in NRK-52E cells (P＜0.05), and SSA at 1 μmol/L significantly inhibited HG-induced upregulation of FN (P＜0.05). The results of network pharmacology showed that there were 35 potential targets of SSA against DN. The top five core targets included epidermal growth factor receptor (EGFR), MYC proto-oncogene (MYC), signal transducer and activator of transcription 3 (STAT3), caspase 3 (CASP3) and Jun proto-oncogene (JUN). GO function and KEGG enrichment analysis showed that the signaling pathways related to the anti-DN mechanisms of SSA were mainly phosphatidylinositol 3-kinase-protein kinase B (PI3K-AKT), advanced glycation end products-receptor for AGE (AGE-RAGE) and epidermal growth factor receptor (EGFR); molecular docking results showed that SSA had a good binding interaction with EGFR, MYC, STAT3, CASP3 and JUN (the binding energy was less than -5 kcal·mol), and could reduce the mRNA levels of EGFR, MYC, STAT3, CASP3 and JUN (P＜0.05). Conclusion: SSA can inhibit the excessive production of ROS and abnormal expression of FN induced by HG, thereby alleviating the damage of NRK-52E cells. The anti-DN mechanisms of SSA may be related to the regulation of EGFR, MYC, STAT3, CASP3, JUN and other targets and PI3K-AKT, AGE-RAGE and EGFR signaling pathways.