Identification of Novel DNA Gyrase B Inhibitors Using Integrated QSAR Modelling, Pharmacophore-Based Virtual Screening and Molecular Docking

Abstract

Antimicrobial resistance (AMR) has become a major concern for global healthcare, reducing the effectiveness of existing antibiotics and emphasizing the need for new antibacterial agents. DNA Gyrase B, which is an essential bacterial enzyme involved in the ATP-dependent DNA supercoiling, is considered a promising therapeutic target because of its role in survival of bacteria and the absence of a human counterpart. The present study employs, an integrated computational approach to facilitate the discovery of potential DNA Gyrase B inhibitors. Machine learning-based Quantitative Structure–Activity Relationship (QSAR) modelling was used to predict the biological activity of reported inhibitors, while a structure-based pharmacophore model was developed for virtual screening of chemical libraries. The identified compounds were subsequently evaluated through drug-likeness assessment, applicability domain analysis, and molecular docking to prioritize promising candidates. This workflow provides a systematic strategy for accelerating antibacterial lead discovery and may support the development of novel therapeutic agents against drug-resistant bacterial infections. Keywords: Antimicrobial Resistance, DNA Gyrase B, QSAR, Machine Learning, Pharmacophore Modelling, Molecular Docking