Abstract
The hepatic manifestation of metabolic syndrome, associated with various metabolic diseases such as type 2 diabetes, insulin resistance, and high cholesterol, is called non-alcoholic fatty liver disease (NAFLD). Despite several research efforts, no approved medicine is currently available for the treatment of this illness. Swiss Target Prediction was used to screen phytochemicals. To examine potential targets, the protein-protein interaction (PPI) network was developed. Cytoscape was used to create the component-target-pathway (C-T-P) network, and AutoDock was used to assess molecular docking. Antioxidant and anti-inflammatory qualities were tested in vitro. Naringenin, ellagic acid, and cyanidin were found to be the main active components. As important targets, PPARA, PPARG, and AKT1 were selected. Through enrichment analysis, a total of 20 crucial signaling pathways, including insulin resistance (IR), NAFLD, relaxin, PI3K-Akt, HIF-1, AGE-RAGE, and MAPK, were identified. The in silico computational techniques predicted the molecular pathway for the active ingredients and the disease targets, thus helping to further research.