What question did this study set out to answer?

This research aims to discover and validate LuxS inhibitors as a way to combat antibiotic resistance.

June 11, 2026

Integrated Pharmacophore, Docking, Machine Learning, and Molecular Dynamics Approach for the Discovery and Validation of LuxS Quorum‐Sensing Inhibitors to Combat Antibiotic Resistance

MNMariyam NawazCOMSATS University Islamabad MAMunir AhmedUnited Arab Emirates University KRKayode RaheemCOMSATS University Islamabad

Key Points

This research aims to discover and validate LuxS inhibitors as a way to combat antibiotic resistance.
Integrated computational framework combining pharmacophore-based virtual screening and machine learning for prioritization.
Molecular dynamics simulations to evaluate binding stability and energetics of identified hits.
Use of residue-level interaction features to refine hit selection in machine learning models.
Top hits showed stable binding profiles during molecular dynamics simulations.
Favorable energetics for selected compounds indicate potential effectiveness as LuxS inhibitors.
The proposed workflow significantly enhances hit prioritization for developing quorum sensing inhibitors.

Abstract

Antibiotic resistance is a major global health problem that reduces the effectiveness of traditional antibiotics. Quorum sensing (QS) refers to a signaling mechanism mediated by small signaling molecules (autoinducers) that regulates the bacterial ability to resist the action of antibiotics. The LuxS enzyme is involved in the QS pathway and cleaves S‐ribosylhomocysteine (SRH) into 4,5‐dihydroxy‐2,3‐pentanedione (DPD), which is then converted into its active form, autoinducer‐2 (AI‐2). This role in the QS pathway makes LuxS an attractive therapeutic target for developing new drugs to inhibit AI‐2 formation. Therefore, this study presents an integrated computational framework combining pharmacophore‐based virtual screening with machine learning (ML)‐guided prioritization to identify potential LuxS inhibitors. Unlike conventional docking approaches, the ML models incorporate residue‐level interaction features to refine hit selection. The identified top hits demonstrated stable binding profiles and favorable energetics during molecular dynamics simulations and MM/PBSA analysis. These findings highlight the effectiveness of the proposed workflow in improving hit prioritization and provide a rational strategy for designing QS inhibitors to combat antibiotic resistance.

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