Inhibitory Activity of Chalcones as MAO‐B Inhibitors: An Insight from Molecular Docking, ADME Analysis, MD Simulation, and MM‐PBSA Calculations
Abstract Alzheimer's disease, which is recognized as a progressive, multifaceted neurodegenerative disorder, is the main factor in late‐life dementia. Monoamine oxidase‐B (MAO‐B) has recently been identified as a potential therapeutic target for Alzheimer's disease (AD). This research explores the inhibitory effects of specific chalcones on MAO‐B utilizing molecular docking, molecular dynamics (MD) simulations, MM‐PBSA free energy calculations, and ADME analysis. The results of molecular docking reveal that the chalcones demonstrate significant binding affinities with MAO‐B, with CHA 22.4 and CHA 22.5 exhibiting the most substantial binding energy of −8.68 and −8.65 kcal/mol, mainly due to hydrogen bonding and hydrophobic interactions. MD simulations validate the stability of protein‐ligand complexes, showing RMSD values stabilizing around 0.4 nm. Meanwhile, MM‐PBSA analysis reveals favorable binding free energies, with CHA 22.5 demonstrating the highest binding energy (−111.27 kJ/mol), indicating a robust interaction with MAO‐B. ADME analysis shows that all chosen chalcones adhere to Lipinski's rule of five and exhibit favorable blood‐brain barrier (BBB) permeability, suggesting possible CNS activity. The results indicate that chalcones may act as effective MAO‐B inhibitors, necessitating additional experimental validation to investigate their therapeutic potential in the treatment of Alzheimer's disease.
Authors
- Mohammed S. Al‐Dosari (ORCID: https://orcid.org/0000-0001-8980-6606)
- Shamama Nishat (ORCID: https://orcid.org/0000-0002-9499-6085)
- Jibin K. Varughese
- Mohammad Khalid Parvez (ORCID: https://orcid.org/0000-0002-7154-9151)
- Thomas Mathew (ORCID: https://orcid.org/0000-0001-5650-5241)
- Nemat Ali (ORCID: https://orcid.org/0000-0001-6525-4202)
- Jisna Jose
- P Rama Devi
Institutions
- King Saud University (SA)
- The Ohio State University Wexner Medical Center (US)
Publication Details
- Journal
- ChemistrySelect
- Published
- 2025-03-30
- DOI
- https://doi.org/10.1002/slct.202404717
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50
Funders
- King Saud University