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

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.