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, Shovanlal Gayen.

Journal Name: Current Drug Discovery Technologies

Volume 14 , Issue 1 , 2017

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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.

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Article Details

VOLUME: 14
ISSUE: 1
Year: 2017
Page: [39 - 52]
Pages: 14
DOI: 10.2174/1570163813666161117144137
Price: $58

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