Virtual Screening–Derived Computational Lead Candidates for MAO-B Inhibition in Parkinson’s Disease: Pathway-Informed Docking, Molecular Dynamics, DFT and ADMET Evaluation

Monoamine oxidase B (MAO-B) is a clinically validated target in Parkinson’s disease (PD), but docking rank alone is insufficient to establish a CNS-directed inhibitor. We integrated MAOB-related pathway context, domain-resolved structural annotation, GNINA virtual screening, predicted blood–brain barrier (BBB) permeation, molecular dynamics (MD), density functional theory (DFT) and ADMET assessment to nominate hypothesis-stage computational candidates. A pharmacophore-derived input collection 2870 entries was subjected to virtual screening; a curated BBB-positive, favorable Vina/CNN-affinity shortlist yielded PubChem CIDs 143667968, 42876521 and 1232631. The three candidates had similar reported GNINA scores. In single 100 ns MD trajectories, CID 42876521 displayed frequent protein–ligand hydrogen bonds, whereas CID 143667968 displayed almost none and reduced aromatic-cage fluctuation. CID 143667968 also had the smallest gas-phase HOMO–LUMO gap. All three compounds carried predicted mutagenicity, carcinogenicity and hERG alerts. CID 42876521 is therefore prioritized for validation, CID 143667968 is retained as a complementary hypothesis and CID 1232631 as a backup scaffold. These results define a preliminary, multi-parameter prioritization framework.

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Journal
International Journal of Molecular Sciences
Published
2026-10-30
DOI
https://doi.org/10.3390/ijms27219781
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Virtual Screening–Derived Computational Lead Candidates for MAO-B Inhibition in Parkinson’s Disease: Pathway-Informed Docking, Molecular Dynamics, DFT and ADMET Evaluation

Ibrahim M. Khater, Modather F. Hussein, Seifeldin Elabed, Khaled M. Alam-ElDein et al.
3 links
International Journal of Molecular Sciences
Computational Drug Discovery Methods
article

Virtual Screening–Derived Computational Lead Candidates for MAO-B Inhibition in Parkinson’s Disease: Pathway-Informed Docking, Molecular Dynamics, DFT and ADMET Evaluation

Ibrahim M. Khater, Modather F. Hussein, Seifeldin Elabed, Khaled M. Alam-ElDein, Ahmed T. Elhendawy, Ahmed A. Aly, Somaia G. Hamada
article en

Abstract

Monoamine oxidase B (MAO-B) is a clinically validated target in Parkinson’s disease (PD), but docking rank alone is insufficient to establish a CNS-directed inhibitor. We integrated MAOB-related pathway context, domain-resolved structural annotation, GNINA virtual screening, predicted blood–brain barrier (BBB) permeation, molecular dynamics (MD), density functional theory (DFT) and ADMET assessment to nominate hypothesis-stage computational candidates. A pharmacophore-derived input collection 2870 entries was subjected to virtual screening; a curated BBB-positive, favorable Vina/CNN-affinity shortlist yielded PubChem CIDs 143667968, 42876521 and 1232631. The three candidates had similar reported GNINA scores. In single 100 ns MD trajectories, CID 42876521 displayed frequent protein–ligand hydrogen bonds, whereas CID 143667968 displayed almost none and reduced aromatic-cage fluctuation. CID 143667968 also had the smallest gas-phase HOMO–LUMO gap. All three compounds carried predicted mutagenicity, carcinogenicity and hERG alerts. CID 42876521 is therefore prioritized for validation, CID 143667968 is retained as a complementary hypothesis and CID 1232631 as a backup scaffold. These results define a preliminary, multi-parameter prioritization framework.

International Journal of Molecular SciencesVol. 27(21)
Cairo University (EG), Qassim University (SA), Zagazig University (EG), Jouf University (SA), Badr University in Cairo (EG), ElSewedy University of Technology (EG), Helwan University (EG)
Good health and well-being
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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