Computational Design and Evaluation of MAO-B Inhibitors for Parkinson’s Disease: Molecular Docking, Qsar Model Pharmacophore Modeling and Admet Prediction
Computational Design and Evaluation of MAO-B Inhibitors for Parkinson’s Disease: Molecular Docking, Qsar Model Pharmacophore Modeling and Admet Prediction
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Original abstract
Abstract Parkinson’s disease (PD) remains a pressing neurodegenerative challenge, with monoamine oxidase B (MAO-B) inhibitors offering therapeutic promise by mitigating oxidative stress and dopaminergic neuron loss. This study integrates molecular docking, Quantitative structure-activity relationship (QSAR), Pharmacophore modeling & ADMET to design and evaluate a hybrid Tacrine-Selegiline MAO-B inhibitors for Parkinson disease. A dataset of structurally diverse Hybrid Tacrine-Selegiline MAO-B inhibitors compounds was curated from literature sources and subjected to high-throughput virtual screening via molecular docking against the MAO-B active site (PDB ID: 2V5Z), yielding binding affinities and Key interactions for Novel Hybrid drug. QSAR analysis employed multiple linear regression and algorithms to correlate molecular descriptors with IC50 data, achieving robust predictive performance (R 2 > 0.94982). Complementary ADMET & Pharmacophore modeling identified critical Pharmacophoric features- such as HBA, HBD and AI validated against known inhibitors. Results highlight top-ranking hybrid derivatives of Tacrine-Selegine 6n & 7d compound with enhanced potency, superior binding scores, outperforming reference standard Selegiline which results as Novel drug for Parkinson Disease.