Revolutionising Antibacterial Warfare: Machine Learning and Molecular Dynamics Unveiling Potential Gram-Negative Bacteria Inhibitors

TL;DR

This study combines machine learning and molecular dynamics to identify potential inhibitors targeting Gram-negative bacteria's drug resistance mechanisms, aiming to combat multi-drug resistance.

Contribution

It provides new insights into bacterial resistance mechanisms and predicts potential efflux pump inhibitors using advanced computational methods.

Findings

Predicted several potential efflux pump inhibitors.

Gained insights into the mechanisms of drug resistance.

Applied machine learning and molecular dynamics for drug discovery.

Abstract

Diseases caused by bacteria have been a threat to human civilisation for centuries. Despite the availability of numerous antibacterial drugs today, bacterial diseases continue to pose life-threatening challenges. The credit for this goes to Gram-Negative bacteria, which have developed multi-drug resistant properties towards \b{eta}-lactams, chloramphenicols, fluoroquinolones, tetracyclines, carbapenems, and macrolide antibiotics. V arious mechanisms of bacterial defence contribute to drug resistance, with Multi-Drug Efflux Pumps and Enzymatic degradation being the major ones. An effective approach to cope with this resistance is to target and inhibit the activity of efflux pumps and esterases. Even though various Efflux Pump Inhibitors and Esterase resistant macrolide drugs have been proposed in the literature, none of them has achieved FDA approval due to several side effects. This research has provided valuable insights into the mechanism of drug resistance by RND efflux pump and Erythromycin esterase. A handful of potential efflux pump inhibitors have been predicted through machine learning and molecular dynamics.