Cheminformatics Analysis of Fluoroquinolones and their Inhibition Potency Against Four Pathogens

Area of Science:

• Medicinal Chemistry
• Cheminformatics
• Computational Biology

Background:

• Drug-resistant bacteria represent a significant global public health challenge.
• The limited pipeline of new antibacterials necessitates exploring structure-activity relationships (SAR) of existing drug classes.
• Fluoroquinolones are a class of bactericides with proven efficacy.

Purpose of the Study:

• To characterize and cluster 507 fluoroquinolone analogues based on structural similarity.
• To analyze the SAR for distinct fluoroquinolone clusters against four major pathogens.
• To develop and validate quantitative structure-activity relationship (QSAR) models for predicting antibacterial potency.

Main Methods:

• Assembly of a dataset of 507 experimentally tested fluoroquinolone analogues.
• Application of cheminformatics techniques for structural characterization and clustering.
• Utilizing machine learning for the development and validation of predictive QSAR models.
• Evaluation of QSAR models using external prediction performance metrics (R² ≥ 0.6, MAE ~ 0.4).

Main Results:

• Identification of both global and local inter-pathogen concordance in fluoroquinolone antibacterial potency.
• Discovery of unique strain selectivity and potency patterns within small fluoroquinolone analogue clusters.
• Development of robust QSAR models with reasonable predictive performance for inhibition potency (pMIC).

Conclusions:

• Fluoroquinolone analogues exhibit pathogen-specific activity patterns that can be leveraged for drug design.
• The developed QSAR models provide a valuable tool for the future design of novel, potent, and selective fluoroquinolone antibacterials.
• This study enhances understanding of fluoroquinolone SAR to combat antimicrobial resistance.