Background Parkinson's disease has been extensively treated with phenyl-based compounds. Various in vivo investigations on phenyl-based derivatives have demonstrated a neuroprotective effect against Parkinson's disease by reducing reactive oxygen species and oxidative stress via the activation of the sirtuin 3. Using a network pharmacology approach, this study intends to evaluate the possible function of phenyl-based derivatives as an anti-Parkinson agent. Materials and Methods The compilation of phenyl-based derivatives utilized numerous open-source databases. The developed compounds were subjected to virtual screening by the hypothesized targets implicated in the development of Parkinson's disease. The drug-likeness score, ADMET profile, and potential adverse effects of the designed ligands were also predicted. Using the Kyoto Encyclopedia of Genes database, the regulated pathways were modeled (KEGG). Based on the number of edge counts, the network between compounds, genes, and pathways with the most modifications was found. Schrödinger suite 2022 was used to undertake the molecular docking and molecular dynamics investigation (100 ns) for the most modulated designed ligand with protein. Results It was discovered that phenyl-based derivatives alter the proteins implicated in the progression of Parkinson's disease. In this study, BZ-1 had the highest number of edge counts and node counts modulating the FOXO1 gene and FOXO signaling pathway.BZ-1 and resveratrol were discovered to have docking scores of -7.936 kcal/mol and − 4.28 kcal/mol, respectively, with an MMGBSA score of -58.77 and − 48.97, respectively.

Background: Scutellaria baicalensis has been extensively used to treat Parkinson's disease. Various in vivo studies on S. baicalensishas shown to decrease oxidative stress and have produced a neuroprotective effect on Parkinson's disease. In addition, the mechanism of action of S. baicalensisin treating Parkinson's disease is not entirely understood. Therefore, this study aims to investigate the potential molecular pathways behind the antiparkinsonian activity of S. baicalensis by employing a network pharmacology methodology. Materials and Methods: Various open-source databases were used in the collection of phytoconstituents. The virtual screening was performed for the hit phytoconstituents by the putative targets implicated in Parkinson's disease development. The phytoconstituent's drug-likeness score, ADMET studies, and probable side effects were also predicted. The regulated pathways were predicted using the Kyoto Encyclopedia of Genes database (KEGG). The network created between phytoconstituents, genes, and pathways for the most modified network was identified based on the number of edge counts. The molecular docking and molecular dynamics study (100 ns) for the highest modulated phytoconstituent with protein was performed using the Schrödinger suite 2022. Results: Seven phytoconstituents from S. baicalensis were discovered to modulate the proteins implicated in Parkinson's disease development. 5-hydroxy-7,8-dimethoxyflavone was determined to have the greatest drug-likeness score with the most number of edge counts and was discovered to have the greatest docking score of 8.337 kcal/mol with the MMGBSA score of -44.33kcal/mol. The neuroactive ligand-receptor interaction pathway was shown to be the most regulated pathway via networking between the phytoconstituent, gene, and pathway.