REVOLUTIONIZING ANTIMICROBIAL DRUG DISCOVERY: COMPUTATIONAL DESIGN AND ADMET STUDIES OF EMERGING POTENT ANTI-MICROBIAL AGENTS

Abstract

Objective: This study focuses on designing potential antimicrobial agents, evaluating their binding affinity against target proteins, and assessing their Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties using computational methods. Methods: This study employed six target proteins from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and utilized Biovia Discovery Studio 2021 for their preparation. Marvin Sketch is used to draw the ten potential candidates and subjected to molecular docking using Python Prescription (PyRx) software. The Biovia Discovery Studio 2021 was used to visualize the docking outcomes, and ADMET properties were determined using Swiss ADME software. Results: Docking experiments conducted on ten derivatives against six protein targets, specifically Sortase-A, Clumping factor A, Undecaprenyl diphosphate synthase, Dehydrosqualene synthase, Tyrosyl tRNA synthetase, and Dihydrofolate reductase. Out of the ten derivatives, compounds 1, 2, 3, 5, and 7 demonstrated a significant binding affinity for one or two target proteins. Notably, compound 8 exhibited exceptional docking scores against five of the six protein targets, establishing itself as the most potent ligand among the compounds tested. These results highlight the paramount significance of compound 8 for subsequent investigation. Furthermore, comprehensive documentation of the physicochemical properties of the potent derivatives was carried out. Conclusion: The findings indicate that the examined compounds have the potential to effectively inhibit various microbial protein targets. In silico ADMET studies suggest that these compounds possess desirable drug-like properties. Therefore, these compounds hold promise as lead molecules for further research, potentially leading to the development of novel antimicrobial drugs.