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Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Research Article

Designing Potential Antitrypanosomal Thiazol-2-ethylamines through Predictive Regression Based and Classification Based QSAR Analyses

Author(s): Sk. Abdul Amin, Nilanjan Adhikari, Sonam Bhargava, Tarun Jha and Shovanlal Gayen

Volume 14, Issue 1, 2017

Page: [39 - 52] Pages: 14

DOI: 10.2174/1570163813666161117144137

Price: $65

Abstract

Background: Thiazol-2-ethylamine is recently reported to be an interesting scaffold having antitrypansomal activity for the treatment of sleeping sickness.

Methods: Statistically significant, robust and validated regression-based QSAR models are constructed for a series of antitrypansomal thiazol-2-ethylamines. Moreover, classification-based QSAR analyses (linear discriminant analysis and Bayesian classification modelling) are also performed to identify the important structural features controlling antitrypanosomal activity.

Results: Molecular fingerprints such as N-piperidinyl and 2-fluorophenyl functions may be responsible for higher antitrypanosomal activity whereas compounds with chlorophenyl moiety and compounds with unsaturated nitrogen atom possess poor activity. These results are supported by the regression-based QSAR model as well as the SAR observations.

Conclusion: Finally, fifteen new compounds bearing thiazol-2-ethylamine scaffold are designed and predicted along with their drug-likeness properties. Therefore, this study may provide important structural aspects of designing new antitrypansomal agents with higher activity.

Keywords: Thiazol-2-ethylamines, antitrypansomal agent, k-MCA, QSAR, MLR, LDA, Bayesian modeling.

Graphical Abstract

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