Molecular Docking, QSAR, and Bioactivity Prediction of Uncaria gambir Flavonoids as Antibacterial Agents Targeting MurA Enzyme

Antimicrobial resistance is a critical global health threat due to the declining effectiveness of existing treatments. The overuse and misuse of antibiotics have accelerated this crisis, rendering many antimicrobial drugs ineffective and leaving previously treatable infections potentially life-threatening. This study employs Quantitative Structure-Activity Relationship (QSAR) modelling as a computational approach to predict the biological activity of novel compounds. QSAR has gained significant recognition in medicinal chemistry and drug discovery due to its ability to establish mathematical correlations between molecular properties and pharmacological effects. The optimal QSAR model was selected via multilinear regression, ensuring statistical robustness. The final model incorporates three key molecular descriptors: electronic, hydrophobic, and steric parameters, which provide a framework for evaluating and designing new antimicrobial agents with improved efficacy. The best QSAR model obtained is LogIC50 = (2.731) - (0.084 x AM1_dipole) + (0.005 x ASA_H) - (0.651 x LogP) - (1.096 x LogS) + (2.863 x mr) - (0.093 x vol). QSAR model predictions, molecular docking, and bioactivity predictions suggest that the novel compound's design can be recommended as an antibacterial, as reflected in lower IC50 values compared to fosfomycin or the original compounds. The integrated computational approach successfully established a predictive QSAR model and identified new inhibitors targeting the MurA enzyme with enhanced efficacy.