Network pharmacology and machine learning reveal multi-target mechanisms of poly herbal formulation against atherosclerosis

Background: Herbs, like Allium sativum, Ginkgo biloba and Nerium oleander are traditional medicinal plants that have been used to treat atherosclerosis and cardiovascular disease. This study provides valuable insights into how network pharmacology (NP) and emerging machine learning are utilized to identify potential drug candidates from these plants for treatment of atherosclerosis. Methods: NP analysis was employed to screen compounds and their potential gene targets from databases and tools e.g. IMPPAT, PubChem, KNAPSACK, Swiss ADME, Swiss Target Prediction, Disgenet and GeneCard. Cytoscape 3.10.2 was employed to visually understand these networks. DAVID database was used for functional and enrichment analysis of the genes validated through molecular docking using PyRx and Discovery Studio. Results: Computational tools and bioinformatics approaches showed a few core compounds, such as Quercetin, Naringenin, Luteolin, Kaempferol, Apigenin, Daidzein, Luteolin-7-olate, Pinocembrin, Pregnenolone and Fisetin found to be effective against atherosclerosis. Pathway analysis revealed that mechanism of atherosclerosis development is directly associated with cholesterol metabolism, cellular senescence, Ras, NF-?B and PI3K-Akt signaling pathways. Conclusion: NP and molecular docking analysis suggested that screened compounds may inhibit progression of atherosclerosis by modulating key associated pathways. Hence, this machine learning aided NP study provides basis for understanding and recognizing the activity of these plants in treating atherosclerosis.