Exploring benzofuran derivatives as selective MAO-B inhibitors: a multilayered in silico study

A total of thirty-seven benzofuran derivatives were analyzed as potential monoamine oxidase B (MAO-B) inhibitors using a variety of computational methods, including extra-precision docking, induced-fit docking (IFD), molecular mechanics/generalized Born surface area (MM/GBSA) re-scoring, ChemPLP re-docking, molecular dynamics (MD), and density functional theory (DFT). Docking simulations confirmed that thirteen of the selected compounds showed activity within the MAO-B active site, whereas only three were also bound to MAO-A. Compounds 10 and 16 exhibited the highest ChemPLP fitness scores (161.60 and 159.45, respectively), comparable to the score of the reference drug safinamide. MD simulations implied that the MAO-B/10 complex maintained a stable ligand RMSD value of approximately 0.3 nm, while the MAO-B/16 complex displayed higher fluctuations. Both complexes exhibited similar root mean square fluctuation (RMSF) and radius of gyration (Rg) profiles. Additionally, the electrostatic potentials, the energies of the HOMO and LUMO, the global reactivity indices, and Mulliken atomic charges of 10 and 16 were also calculated through DFT (B3LYP/ 6-311 ++ G(d,p) level of theory). The drug-likeness and the predicted pharmacokinetic properties of the two compounds were additionally evaluated with SwissADME. Based on the conducted in silico study, compounds 10 and 16 emerged as the most promising candidates for further in vitro and in vivo validation.

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Publication Details

Journal
Pharmacia
Published
2026-09-16
DOI
https://doi.org/10.3897/pharmacia.73.e206275
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring benzofuran derivatives as selective MAO-B inhibitors: a multilayered in silico study

Alexander Zlatkov, Muhammed Tılahun Muhammed, Stefan Kostov, Ali Irfan et al.
Pharmacia
Parkinson's Disease Mechanisms and Treatments
article

Exploring benzofuran derivatives as selective MAO-B inhibitors: a multilayered in silico study

Alexander Zlatkov, Muhammed Tılahun Muhammed, Stefan Kostov, Ali Irfan, Емилио Матеев
article en

Abstract

A total of thirty-seven benzofuran derivatives were analyzed as potential monoamine oxidase B (MAO-B) inhibitors using a variety of computational methods, including extra-precision docking, induced-fit docking (IFD), molecular mechanics/generalized Born surface area (MM/GBSA) re-scoring, ChemPLP re-docking, molecular dynamics (MD), and density functional theory (DFT). Docking simulations confirmed that thirteen of the selected compounds showed activity within the MAO-B active site, whereas only three were also bound to MAO-A. Compounds 10 and 16 exhibited the highest ChemPLP fitness scores (161.60 and 159.45, respectively), comparable to the score of the reference drug safinamide. MD simulations implied that the MAO-B/10 complex maintained a stable ligand RMSD value of approximately 0.3 nm, while the MAO-B/16 complex displayed higher fluctuations. Both complexes exhibited similar root mean square fluctuation (RMSF) and radius of gyration (Rg) profiles. Additionally, the electrostatic potentials, the energies of the HOMO and LUMO, the global reactivity indices, and Mulliken atomic charges of 10 and 16 were also calculated through DFT (B3LYP/ 6-311 ++ G(d,p) level of theory). The drug-likeness and the predicted pharmacokinetic properties of the two compounds were additionally evaluated with SwissADME. Based on the conducted in silico study, compounds 10 and 16 emerged as the most promising candidates for further in vitro and in vivo validation.

PharmaciaVol. 73
Medical University of Sofia (BG), Institute of Management Sciences Lahore (PK), Suleyman Demirel University (KZ)
Medical University Sofia
Openalex Percentile: Top 12%
Parkinson's Disease Mechanisms and Treatments
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Exploring benzofuran derivatives as selective MAO-B inhibitors: a multilayered in silico study — Alexander Zlatkov, Muhammed Tılahun Muhammed, et al. · Pharmacia (2026) | TGRS Research Map | TGRS