Flavonoid as a Potent Antioxidant: Quantitative Structure-Activity Relationship Analysis, Mechanism Study, and...

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Materials Science

Background:

• Flavonoids are known for their antioxidant properties, but optimizing their lipid solubility and activity is challenging.
• Understanding structure-activity relationships is crucial for designing effective antioxidant compounds.

Purpose of the Study:

• To develop a quantitative structure-antioxidant activity relationship (QSAR) model for flavonoids using machine learning (ML).
• To design novel, lipid-soluble flavonoids with enhanced antioxidant capabilities.
• To identify key structural features contributing to antioxidant activity and solubility.

Main Methods:

• Designed 398 flavonoid molecular structures with diverse substituents based on the flavonoid skeleton.
• Calculated hydrogen dissociation energies (ΔG1, ΔG2, ΔG3) and solubility parameters (δ) using molecular simulation.
• Employed artificial neural network and random forest models for ML analysis and prediction.

Main Results:

• Identified optimal substitution patterns: hydroxyl groups at B4', B5', B6' and a branched alkyl group at C3.
• Designed flavonoids showed improved antioxidant activity and compatibility with apolar organic materials.
• The optimized flavonoid decreased ΔG1 by 2.2% and δ by 15.1% compared to a pyrogallol-grafted reference.

Conclusions:

• Efficient flavonoids require multiple ortho-phenolic hydroxyl groups and strategically placed hydrophobic groups.
• The combined approach of molecular simulation and ML offers a novel strategy for designing antioxidants.
• This study provides a framework for the rational design of functional flavonoids.