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

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

Volume: 14 Issue: 8

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

Affiliation:

• Faculty of Life Sciences, Amazonian State University, Paso Lateral km 2½ via Tena, Puyo, Pastaza,Ecuador

• Department of Systems and Computer Engineering, Carleton University, Ottawa, ON,Canada

Keywords: α-Amylase, α-glucosidase, classification model, dragon descriptor, machine learning, QSAR.

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.

Cite this article as:

Dieguez-Santana Karel*, Pham-The Hai, Rivera-Borroto M. Oscar, Puris Amilkar, Le-Thi-Thu Huong and Casanola-Martin M. Gerardo*, A Two QSAR Way for Antidiabetic Agents Targeting Using α-Amylase and α-Glucosidase Inhibitors: Model Parameters Settings in Artificial Intelligence Techniques, Letters in Drug Design & Discovery 2017; 14 (8) . https://dx.doi.org/10.2174/1570180814666161128121142

DOI https://dx.doi.org/10.2174/1570180814666161128121142	Print ISSN 1570-1808
Publisher Name Bentham Science Publisher	Online ISSN 1875-628X