Computational Identification of Coumarin Derivatives as a Potential MAO Inhibitors

Abstract

This study explores the neurotherapeutic potential of tailored coumarin (2H-1-benzopyran-2-one) derivatives engineered to target the complex pathological pathways of depression. Leveraging the structural versatility of the core oxygen-containing heterocycle, we investigated how strategic pharmacophoric substitutions at the C-3, C-4 and C-7 positions modulate target binding affinity. To evaluate these structural modifications, an In silico molecular docking screen was conducted using AutoDock Vina, mapping geometric conformations and calculating binding free energies through its empirical scoring parameters. Top-scoring structural variants were further analyzed using BIOVIA Discovery Studio Visualizer to map specific non-covalent interactions—including hydrogen bonding network networks, hydrophobic pockets and (pi)-stacking alignments—within the receptor active sites. The computational workflow successfully identified several standout coumarin variants characterized by exceptional binding affinities and favorable thermodynamic stability. Detailed visual profiling confirmed precise atomic alignment and robust structural coordination within the targeted biological domains. Taken together, these computational insights validate the coumarin framework as a highly adaptable structural scaffold for antidepressant discovery, establishing a reliable chemical blueprint for upcoming bench synthesis and biological assays.