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
Related Journals
Anti-Cancer Agents in Medicinal Chemistry
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry
Current Computer-Aided Drug Design
Current Bioactive Compounds
Current Cancer Drug Targets
Combinatorial Chemistry & High Throughput Screening
Current Cancer Therapy Reviews
Current Diabetes Reviews
Current Drug Safety
Current Drug Targets
Related Books
Drug Addiction Mechanisms in the Brain
Software and Programming Tools in Pharmaceutical Research
Objective Pharmaceutics: A Comprehensive Compilation of Questions and Answers for Pharmaceutics Exam Prep
Medicinal Chemistry of Drugs Affecting Cardiovascular and Endocrine Systems
Medicinal Plants, Phytomedicines and Traditional Herbal Remedies for Drug Discovery and Development against COVID-19
Advanced Pharmacy
Plant-derived Hepatoprotective Drugs
The Role of Chromenes in Drug Discovery and Development
New Avenues in Drug Discovery and Bioactive Natural Products
Practice and Re-Emergence of Herbal Medicine
40
8