Development of QSAR models using neural networks for the estimation of potential a-glucosidase inhibitors from phenolics found in food matrices
DOI:
https://doi.org/10.29059/cienciauat.v20i1.2041Keywords:
neural network, predictive model, molecular docking, phenolic compounds, enzyme inhibitionAbstract
a-Glucosidase is a key enzyme in carbohydrate digestion, and its inhibition represents a therapeutic strategy for the management of type 2 diabetes. The aim of this study was to develop a QSAR model using an artificial neural network (ANN) combined with molecular docking to estimate the inhibitory activity of a-glucosidase by phenolic compounds found in food matrices (PC-FM). The ANN was trained to predict pIC50 values using different configurations of hidden-layer neurons. The best estimation of pIC50 was achieved using an RNA design with 7 neurons in the occult layer and two molecular descriptors (SpMin7_Bhm, AATSC5s). Molecular docking was performed between a-glucosidase (isomaltase) and phenolic compounds that showed predicted pIC50 values equal to or higher than acarbose (n = 17). According to the model, naringin and quercetin 3,4'-O-diglucoside exhibited the lowest predicted pIC50 values (−1.96) and the highest binding affinities (−10.6 kcal/mol and −10.4 kcal/mol, respectively), outperforming the reference compound acarbose (pIC50 = −2.00; binding affinity = −9.8 kcal/mol). The integration of ANN-based QSAR modeling and molecular docking provides an efficient tool for screening the inhibitory potential of phenolic compounds against a-glucosidase enzyme.
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