Computational screening of natural compounds from traditional Persian medicinal plant for the finding of potential antidepressant.

Depression is one of the most common mental disorders, and more than half of individuals with depression have reduced or discontinued antidepressant use due to side effects. Medicinal plants are safer, cheaper, and more readily available than synthetic medications. This study will employ bioinformatics to assess the impact of compounds discovered in plants used in traditional Persian medicine (TPM) on key proteins involved in depressive pathways. Compounds derived from plants used in TPM for the treatment of depression were identified and subsequently evaluated for ADME properties and toxicity. The potential target genes of these compounds, along with genes associated with depression, were identified, and the intersecting genes were selected to construct a protein-protein interaction (PPI) network. Molecular docking analysis of the plant compounds and their key target proteins was conducted, followed by the selection of the most effective compound based on molecular dynamics (MD) simulation. In this study, 23 genes were identified as target genes of the plant compounds, and 582 genes associated with depression were identified, nine of which were shared, including ABCB1, AKT1, CAT, CDH1, CYP2B6, ESR1, ESR2, PPARG, and TRPV1. The PPI network identified ABCB1, AKT1, CDH1, ESR1, and PPARG as key proteins. Among them, ABCB1 and AKT1 exhibited favorable docking energies with carnosic acid. MD simulations further revealed the stability of the ABCB1-carnosic acid and AKT1-carnosic acid complexes. Our findings indicate that carnosic acid may represent a potential therapeutic option for the treatment of depression. However, further in vivo and clinical trials are required to validate these findings.