Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Mycology

Background:

• Fungal infections pose a significant threat to agriculture and human health.
• Cinnamate derivatives have shown potential as antifungal agents.
• Predictive modeling can accelerate the discovery of new fungicides.

Purpose of the Study:

• To establish Quantitative Structure-Activity Relationship (QSAR) models for cinnamate derivatives.
• To analyze the fungicidal activities against Pythium sp and Corticium rolfsii.
• To predict the activity of novel cinnamate compounds.

Main Methods:

• Calculated over a thousand molecular descriptors using Dragon software.
• Developed QSAR models correlating descriptors with fungicidal activity.
• Synthesized and evaluated 21 new cinnamate derivatives.

Main Results:

• QSAR models accurately predicted experimental fungicidal activity.
• Compounds 38, 28, and 42 showed high predicted activity against Pythium sp.
• Compounds 28 and 34 demonstrated high predicted activity against C. rolfsii.

Conclusions:

• QSAR modeling is effective for predicting fungicidal activity of cinnamate derivatives.
• Identified promising novel compounds for further development as fungicides.
• The study provides a foundation for designing more potent antifungal agents.