Abstract
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 screenings pipelines.Keywords: α-Amylase, α-glucosidase, classification model, dragon descriptor, machine learning, QSAR.
Graphical Abstract
Call for Papers in Thematic Issues
Navigating the Frontier: Probiotics' Promising Path in Combatting Viral Respiratory Infections, Spotlight on Coronaviridae.
Probiotics, well-known for their role in gut health, show promise in preventing and managing respiratory virus infections, particularly in children and at-risk populations. However, the profound influence of novel pathogenic viral RTIs such as coronaviruses has reshaped perspectives on the potential of probiotics. Despite their potential, significant gaps remain in ...read more
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