Structure-based optimization of azole antifungal agents by CoMFA, CoMSIA, and molecular docking

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Azole compounds are crucial in antifungal therapy.
• Developing potent antifungal agents remains a significant challenge.
• Understanding drug-target interactions is key for drug design.

Purpose of the Study:

• To design and synthesize novel, potent azole antifungal agents.
• To explore the binding interactions of azoles with lanosterol 14alpha-demethylase.
• To establish a reliable computational model for guiding antifungal drug discovery.

Main Methods:

• Utilized 3D-Quantitative Structure-Activity Relationship (3D-QSAR) methods, including CoMFA and CoMSIA.
• Employed flexible docking simulations to investigate compound binding at the enzyme's active site.
• Designed and synthesized 57 novel azole compounds based on molecular modeling insights.

Main Results:

• Identified key hydrophobic, van der Waals, pi-pi stacking, and hydrogen bonding interactions.
• Developed a receptor-based pharmacophore model for rational drug design.
• Achieved significantly improved in vitro antifungal activity with the novel azole compounds.

Conclusions:

• The developed computational model accurately predicts and guides the optimization of azole antifungal agents.
• The synthesized novel azoles exhibit enhanced potency, confirming the model's reliability.
• This study provides a foundation for the rational design of next-generation antifungal therapies.