Drug target insights · 2026
Optimizing ChEMBL-derived QSAR models for natural flavonoid screening: chemotype-specific predictive reliability in α-glucosidase-inhibition.
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Zakaria Radhiah, Iqhrammullah Muhammad, de Liyis Bryan Gervais, Susilawati Anggi, Albar Nizam, Kacaribu Andriy Anta, Habiburrahman Muhammad
Аннотация
INTRODUCTION: α-Glucosidase inhibitors (AGIs) are essential for controlling postprandial hyperglycemia, with flavonoids representing a major class of natural AGIs. However, conventional quantitative structure-activity relationship (QSAR) models trained on structurally diverse datasets often exhibit limited predictive reliability for natural flavonoids due to chemical space mismatch. This study aimed to develop and optimize Random Forest-based QSAR models derived from ChEMBL AGIs and to evaluate their chemotype-specific applicability to natural flavonoids.
METHODS: ChEMBL-derived inhibitors were curated into two datasets: (i) a general scaffold-diverse dataset and (ii) a flavonoid-specific dataset. A custom RDKit-based pipeline employing SMARTS pattern recognition and fingerprint similarity automatically classified flavonoid chemotypes while excluding nonphenolic compounds. Molecular descriptors were calculated using Mordred. Regression and classification models were constructed with 10-fold cross-validation. External validation was performed using 15 literature-reported natural flavonoids categorized into flavones/flavonols (aglycones), flavonol glycosides, isoflavones, and flavan-3-ols.
RESULTS: The general model (n = 563) demonstrated strong regression performance (R2 = 0.837) and classification accuracy (0.915). The flavonoid-specific model showed moderate regression (R2 = 0.564) and classification accuracy (0.880). However, external validation revealed superior predictive reliability of the flavonoid-specific model, particularly for catechins (MAE = 0.182) and aglycones (MAE = 0.233). Predictive performance decreased for glycosides and isoflavones.
CONCLUSION: Chemotype-focused QSAR modeling enhances predictive reliability for natural flavonoids. The optimized flavonoid-specific model is suitable for predicting pIC50 of flavones and flavonols but should be applied cautiously to glycosylated or structurally divergent subclasses.
METHODS: ChEMBL-derived inhibitors were curated into two datasets: (i) a general scaffold-diverse dataset and (ii) a flavonoid-specific dataset. A custom RDKit-based pipeline employing SMARTS pattern recognition and fingerprint similarity automatically classified flavonoid chemotypes while excluding nonphenolic compounds. Molecular descriptors were calculated using Mordred. Regression and classification models were constructed with 10-fold cross-validation. External validation was performed using 15 literature-reported natural flavonoids categorized into flavones/flavonols (aglycones), flavonol glycosides, isoflavones, and flavan-3-ols.
RESULTS: The general model (n = 563) demonstrated strong regression performance (R2 = 0.837) and classification accuracy (0.915). The flavonoid-specific model showed moderate regression (R2 = 0.564) and classification accuracy (0.880). However, external validation revealed superior predictive reliability of the flavonoid-specific model, particularly for catechins (MAE = 0.182) and aglycones (MAE = 0.233). Predictive performance decreased for glycosides and isoflavones.
CONCLUSION: Chemotype-focused QSAR modeling enhances predictive reliability for natural flavonoids. The optimized flavonoid-specific model is suitable for predicting pIC50 of flavones and flavonols but should be applied cautiously to glycosylated or structurally divergent subclasses.