Background: This work showed the use of 0-2D Dragon molecular descriptors in the
prediction of α-amylase and α-glucosidase inhibitory activity.
Methods: Several artificial intelligence techniques are used for obtaining quantitative structure-activity
relationship (QSAR) models to discriminate active (inhibitor) compounds from inactive (non-inhibitor)
ones. The machine learning methodologies such as support vector machine, artificial neural network, and
k-nearest neighbor (k-NN) were employed. The k-NN technique had the best classification performances
for both targets with values above 90% for the training and prediction sets, correspondingly.
Results and Conclusion: These results provided a double target modeling approach for increasing the
estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual
Keywords: α-Amylase, α-glucosidase, classification model, dragon descriptor, machine learning, QSAR.
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