Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.
Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.
Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.
OBJECTIVES: To identify the important pharmacophoric features and correlate 3D chemical structure of benzothiazinimines with their anti-HIV potential using 2D, 3D-QSAR and pharmacophore modeling studies.
METHODS: QSAR and pharmacophore mapping studies have been used to relate structural features. 2D QSAR and 3D QSAR studies were performed using partial least square and k-nearest neighbor methodology, coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing, to derive QSAR models which were further validated for statistical significance.
RESULTS: The physicochemical descriptor XAHydrophilicArea and SsOHE-index, and alignmentindependent descriptor T_C_Cl_6 showed significant correlation with the anti-HIV activity of benzothiazinimines in 2D QSAR. 3D QSAR results showed the significant effect of electrostatic and steric field descriptors in the anti-HIV potential of benzothiazinimines. The generated pharmacophore hypothesis demonstrated the importance of aromaticity and hydrogen bond acceptors.
CONCLUSION: The significant models obtained in this study suggested that these techniques could be used as a guidance for designing new benzothiazinimines with enhanced anti-HIV potential.