Abstract

UNVEILING THE POTENTIAL OF CHAMOMILE AND LAVENDER PHYTOCONSTITUENTS AS ANTI-DEPRESSION AGENTS: AN IN SILICO MOLECULAR DOCKING ANALYSIS

Madhurita Chakrabarti*

ABSTRACT

Objective: This investigation aims to explore the potential of phytoconstituents from Chamomile and Lavender as anti-depressant compounds. The focus is on understanding their molecular interactions and binding affinities with the dopamine transporter and human serotonin transporter. Materials and methods: The structures of the phytoconstituents of Chamomile and Lavender were obtained from the PubChem database and converted into pdb format. The protein targets dopamine transporter (PDB ID: 4M48) and human serotonin transporter (PDB ID: 6AWP) were obtained from the Protein Data Bank and converted into PDBQT format. The molecular docking analysis was studied using PyRx software to predict binding affinities and interaction modes. Docking outcomes were visualized using Biovia Discovery Studio 2021. Results: The molecular docking analysis revealed promising findings. Phytoconstituents from Chamomile and Lavender showed significant binding affinities with the targeted receptors. Matricin exhibited the highest binding affinity of -9.1 kcal/mol with the dopamine transporter, while Patuletin showed an excellent docking score of -8.5 kcal/mol with the human serotonin transporter. Beta-caryophyllene demonstrated the highest binding score of -8.9 kcal/mol with the dopamine transporter and -7.9 kcal/mol with the human serotonin transporter. Conclusions: This study contributes to the research on natural remedies for depression. Chamomile and Lavender phytoconstituents show potential as bioactive compounds with anti-depressant properties. Further experimental investigations are needed to validate their therapeutic potential. Utilizing natural products in the treatment of depression can offer alternative options and improve patient outcomes.

Keywords: Phytoconstituents, Chamomile, Lavender, Anti-depressant compounds, Molecular docking analysis, PyRx software.