DFT based QSAR study on quinolone-triazole derivatives as antibacterial agents

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Quinolone-triazole derivatives are investigated for their antibacterial properties.
• Understanding structure-activity relationships is crucial for drug discovery.

Purpose of the Study:

• To develop robust QSAR models for quinolone-triazole derivatives against Gram-positive (S. aureus) and Gram-negative (P. aeruginosa) bacteria.
• To identify key molecular descriptors that govern antibacterial activity.
• To design novel compounds with enhanced antibacterial efficacy.

Main Methods:

• Quantitative Structure-Activity Relationship (QSAR) modeling.
• Density Functional Theory (DFT) calculations for molecular optimization and descriptor extraction.
• Hierarchical cluster analysis for dataset division.
• Stepwise Multiple Linear Regression (MLR) for model development.
• Internal and external validation of QSAR models.

Main Results:

• Developed predictive QSAR models for S. aureus (R² = 0.889, R²ext = 0.938) and P. aeruginosa (R² = 0.957, R²ext = 0.923).
• Identified critical descriptors: partial atomic charges, bond length, 13C-NMR chemical shift for S. aureus; partial atomic charge, LUMO-HOMO energy gap, logP for P. aeruginosa.
• Designed and proposed new quinolone-triazole derivatives with potentially higher antibacterial activity.

Conclusions:

• QSAR modeling effectively predicts the antibacterial activity of quinolone-triazole derivatives.
• Specific molecular descriptors significantly influence activity against S. aureus and P. aeruginosa.
• The study provides a foundation for the rational design of novel antibacterial agents.