ArticleScientific reports2025
Predicting flavonoid physicochemical properties using topological indices and regression modeling.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Extracellular vesicle delivery of Myricetin suppresses ovarian cancer through mitochondrial dynamics by downregulating the ECM1/NF-κB/TGFβ signaling pathway.Scientific reports · 2026Article
- Predictive modeling of influenza strain drugs using temperature-based topological indices and regression analysis via multi-criteria decision making techniques.Scientific reports · 2026Article
- Eccentricity-based topological indices of QSPR modelling using anti-cancer drugs.Frontiers in chemistry · 2026Article
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7 authors.
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Abstract
Flavonoids, a diverse class of polyphenolic phytochemicals, exhibit multifaceted biological activities critical to human health. This study leverages degree-based topological indices (TIs) to predict six physicochemical properties of sixty flavonoids using linear, quadratic, and logarithmic regression models. Statistical validation via correlation coefficients ([Formula: see text]), Root Means Square Error (RMSE), and Mean Absolute Error (MAE) revealed robust predictive power, particularly for molar refractivity ([Formula: see text], RMSE [Formula: see text], MAE [Formula: see text]), molar volume ([Formula: see text], RMSE [Formula: see text], MAE [Formula: see text]), and enthalpy of vaporization ([Formula: see text], RMSE [Formula: see text], MAE [Formula: see text]). Quadratic models consistently outperformed linear/logarithmic approaches, indicating nonlinear relationships between TIs and properties. The methodology offers a cost-effective tool for prioritizing bioactive flavonoids in drug discovery, validated by strong agreement between predicted and experimental values for external compounds (e.g., Procyanidin B2: molar refractivity RMSE [Formula: see text]). This work bridges cheminformatics and QSPR, enabling rapid property estimation for polyphenolic systems.
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