INTERNATIONAL JOURNAL OF ADVANCE RESEARCH
IN MULTIDISCIPLINARY

Antimicrobial resistance (AMR) is increasingly limiting the effectiveness of established antibiotics and creating an urgent need for alternative therapeutic strategies. Conventional antimicrobial discovery is a lengthy and expensive process, while the emergence of resistance can rapidly reduce the clinical usefulness of newly introduced agents. Computational drug discovery and drug repurposing offer complementary approaches for accelerating the identification of potential antimicrobial candidates. The present paper examines an integrated computational framework combining molecular docking, molecular dynamics simulation and pharmacological prediction for identifying and prioritizing compounds capable of acting against resistant bacterial pathogens.

The proposed approach begins with the identification of clinically relevant bacterial targets associated with essential cellular functions and resistance mechanisms. Compound libraries can subsequently be filtered using molecular properties, structural quality and drug-likeness criteria before molecular docking is employed to estimate ligand-target interactions. High-priority complexes can then be examined through molecular dynamics simulations to assess structural stability and persistence of important interactions. Pharmacological prediction, including absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment, is incorporated to eliminate candidates with unfavourable drug-development characteristics. Drug repurposing is further integrated into the framework because previously characterized compounds may provide opportunities for more rapid development than completely novel chemical entities.