# Cross-Database Characterization of Flavonoids and Phenolic Acids: Integrating Drug-likeness Metrics, Molecular Interactions, and Dietary Sources

## TL;DR

This study evaluates the drug potential and food sources of flavonoids and phenolic acids using a unified database approach.

## Contribution

A novel integrative framework combining drug-likeness metrics, molecular interactions, and dietary sources for flavonoids and phenolic acids.

## Key findings

- Isoflavones showed the best drug-likeness profiles with a mean QED score of 0.62.
- Flavonoids had higher binding affinities and targeted more proteins than phenolic acids.
- Herbs and spices were identified as the richest sources of these compounds.

## Abstract

Background: Flavonoids and phenolic acids are recognized for their diverse therapeutic potential, yet their translation into clinical applications remains limited by varying bioavailability and fragmented characterization across databases. A systematic integrative approach is needed to comprehensively evaluate these compounds’ drug-likeness properties based on computational metrics, molecular interactions, and dietary sources within a unified framework. Methods: We analyzed 954 compounds (715 flavonoids, 239 phenolic acids) by integrating data from PhytoHub, Phenol-Explorer, ChEMBL, and FoodDB databases. Drug-likeness was assessed using established metrics, including QED (Quantitative Estimate of Drug-likeness) and DataWarrior drug-likeness scores. Molecular interaction patterns were characterized through ChEMBL activity data, and food source distributions were systematically mapped across major food groups. Results: Drug-likeness assessment revealed complementary evaluation patterns between QED (mean = 0.48 ± 0.24) and DataWarrior scores (mean = −2.46 ± 4.38), with moderate inter-correlation (r = 0.41), indicating that each metric captures distinct aspects of molecular properties. Isoflavones demonstrated the most favorable drug-likeness profiles (mean QED: 0.62 ± 0.18). Molecular interaction analysis demonstrated significantly higher binding affinities for flavonoids (mean ChEMBL activity score: 7.26 ± 1.09) compared to phenolic acids (6.98 ± 0.94, p = 0.014), with flavonoids targeting a broader range of proteins (67 unique targets vs. 33 for phenolic acids). Food source mapping identified herbs and spices as the richest sources (up to 14,500 mg/kg), followed by fruits (40,490 mg/kg total) and teas (37,101 mg/kg total), with distinct compound distribution patterns across food groups. Conclusions: This integrative cross-database approach provides a comprehensive characterization framework for flavonoids and phenolic acids, combining established drug-likeness metrics, molecular interaction analysis, and dietary source mapping. The methodology establishes a systematic foundation for compound evaluation in drug development and nutritional research.