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: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)
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.
