Computational and Pharmaceutical Studies of Chalcone Analogues as Potential Carbonic Anhydrase Inhibitors
DOI:
https://doi.org/10.70749/ijbr.v3i8.2250Keywords:
Chalcone Analogues, Carbonic Anhydrase, Molecular Docking, DFT, QSAR, Drug DesignAbstract
Chalcone derivatives, a prominent class of α,β-unsaturated ketones, are well-known for their diverse pharmacological properties including anticancer, antimicrobial, anti-inflammatory, and antioxidant activities. Due to their structural flexibility and ease of synthesis, chalcones have become attractive scaffolds in drug discovery. In the present study, a comprehensive computational investigation was conducted to evaluate a library of chalcone analogues as potential inhibitors of human carbonic anhydrase-I (hCA-I), an enzyme implicated in numerous physiological and pathological processes such as glaucoma, cancer, edema, and epilepsy. Molecular docking simulations were performed using Maestro Schrödinger and Molecular Operating Environment (MOE) to predict the binding affinity and interaction patterns of the chalcone analogues with the active site of hCA-I (PDB ID: 5E2M). The docking scores ranged from -8.036 to -2.732 kcal/mol, indicating strong binding affinity. Notably, several chalcone derivatives exhibited better binding energies than the standard carbonic anhydrase inhibitor, Acetazolamide (AZA), which had a docking score of -6.246 kcal/mol. Compounds 73, 74, 77, and 102 emerged as top candidates based on their high docking scores. To further evaluate their molecular properties, Density Functional Theory (DFT) calculations were conducted using Gaussian09 software. Parameters such as HOMO-LUMO energy gaps and Molecular Electrostatic Potential (MEP) surfaces were analyzed to understand the electronic distribution and chemical reactivity of the ligands. Drug-likeness was also assessed using the Molsoft tool, and favorable scores were observed for most top-scoring compounds, suggesting good pharmacokinetic potential. Additionally, Quantitative Structure–Activity Relationship (QSAR) modeling was carried out to correlate molecular descriptors with biological activity, reinforcing the predictive power of the computational models. Collectively, the results underscore the potential of chalcone analogues as effective hCA-I inhibitors and encourage further experimental validation for therapeutic development.
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