Computational Identification of Bacterial Efflux Pump Inhibitors: In Silico Strategies to Overcome Drug Resistance

गोषवारा

One of the biggest threats to the wellness of the planet is the emerging antimicrobial resistance (AMR), where bacterial efflux pumps contribute significantly to creating multidrug resistance by directly pumping antibiotics out of the cells. The use of efflux pump inhibitors (EPIs) has become one of the new promising approaches to reinstate the effectiveness of already existing antibiotics. It gives a survey of computational methods used in the discovery and optimization of EPIs and gives emphasis to molecular docking, molecular docking together with virtual screening, molecular dynamics (MD) simulations, machine learning models and ADMET prediction models. Some of the key databases and resources, including ZINC, PubChem, ChEMBL and RCSB PDB, as well as AlphaFold, are mentioned with the references to target integration of ligand and protein data.

This review performs comparisons of methodology, predictive accuracies, and validation outcomes through four case studies, against targets in Staphylococcus aureus (Targeting NorA), Escherichia coli (Targeting AcrAB-TolC), Mycobacterial efflux pumps, and machine learning-driven screening. It is also strongly evaluating the existing obstacles such as scarce structural information, unreliability of docking scores and lack of biological validation. In future projections, the combination of artificial intelligence, the structural elucidation by cryo-EM, systems biology, and personalization based on the genomics is outlined as emerging tendencies. This review would conclude that to a large extent, one can argue that in silico methods are very powerful and promising in rational discovery of Episodes, but that multidisciplinary work between computational and experimental research needs to take place.