A Two QSAR Way for Antidiabetic Agents Targeting Using α-Amylase and α-Glucosidase Inhibitors: Model Parameters Settings in Artificial Intelligence Techniques

Author(s): Karel Dieguez-Santana*, Hai Pham-The, Oscar M. Rivera-Borroto, Amilkar Puris, Huong Le-Thi-Thu, Gerardo M. Casanola-Martin*.

Journal Name: Letters in Drug Design & Discovery

Volume 14 , Issue 8 , 2017

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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.

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Article Details

VOLUME: 14
ISSUE: 8
Year: 2017
Page: [862 - 868]
Pages: 7
DOI: 10.2174/1570180814666161128121142
Price: $58

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