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.
Current Drug Discovery Technologies
Title:Designing Potential Antitrypanosomal Thiazol-2-ethylamines through Predictive Regression Based and Classification Based QSAR Analyses
Volume: 14 Issue: 1
Author(s): Sk. Abdul Amin, Nilanjan Adhikari, Sonam Bhargava, Tarun Jha and Shovanlal Gayen
Affiliation:
Keywords: Thiazol-2-ethylamines, antitrypansomal agent, k-MCA, QSAR, MLR, LDA, Bayesian modeling.
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.Export Options
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Cite this article as:
Amin Abdul Sk., Adhikari Nilanjan, Bhargava Sonam, Jha Tarun and Gayen Shovanlal, Designing Potential Antitrypanosomal Thiazol-2-ethylamines through Predictive Regression Based and Classification Based QSAR Analyses, Current Drug Discovery Technologies 2017; 14 (1) . https://dx.doi.org/10.2174/1570163813666161117144137
DOI https://dx.doi.org/10.2174/1570163813666161117144137 |
Print ISSN 1570-1638 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6220 |
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