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Current Computer-Aided Drug Design

Author(s): Karel Diéguez-Santana*, Amilkar Puris, Oscar M. Rivera-Borroto, Gerardo M. Casanola-Martin, Bakhtiyor Rasulev and Humberto González-Díaz

DOI: 10.2174/1573409918666220929124820

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A Fuzzy System Classification Approach for QSAR Modeling of α- Amylase and α-Glucosidase Inhibitors

Page: [469 - 479] Pages: 11

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Abstract

Introduction: This report proposes the application of a new Machine Learning algorithm called Fuzzy Unordered Rules Induction Algorithm (FURIA)-C in the classification of druglike compounds with antidiabetic inhibitory ability toward the main two pharmacological targets: α-amylase and α-glucosidase.

Methods: The two obtained QSAR models were tested for classification capability, achieving satisfactory accuracy scores of 94.5% and 96.5%, respectively. Another important outcome was to achieve various α-amylase and α-glucosidase fuzzy rules with high Certainty Factor values. Fuzzyrules derived from the training series and active classification rules were interpreted. An important external validation step, comparing our method with those previously reported, was also included.

Results: The Holm’s test comparison showed significant differences (p-value<0.05) between FURIA-C, Linear Discriminating Analysis (LDA), and Bayesian Networks, the former beating the two latter according to the relative ranking score of the Holm’s test.

Conclusion: From these results, the FURIA-C algorithm could be used as a cutting-edge technique to predict (classify or screen) the α-amylase and α-glucosidase inhibitory activity of new compounds and hence speed up the discovery of new potent multi-target antidiabetic agents.

Keywords: Anti-diabetic Agents, induction rule, FURIA-C, QSAR, Machine-learning techniques

Graphical Abstract

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