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

[ Vol. 14 , Issue. 1 ]

Author(s):

Sk. Abdul Amin, Nilanjan Adhikari, Sonam Bhargava, Tarun Jha and Shovanlal Gayen   Pages 39 - 52 ( 14 )

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.

Affiliation:

Natural Science Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, (WB), Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University (A Central University), Sagar 470003, (MP)

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