Cytotoxicity, 2D- and 3D- QSAR Study of some Halogen Containing Hydroxy and Amino Substituted Aromatic Compounds
M. Idrish Ali, Muhammad Abul Kashem Liton
Abstract
M. Idrish Ali, Muhammad Abul Kashem Liton
Abstract
A set of 24 halogen containing hydroxy and amino substituted aromatic compounds were subjected to 2D- and 3D-QSAR studies. 3D-QSAR was studied at a 2.0 Ǻ 3D grid spacing using molecular interaction fields (MIFs) analysis. The best predictive models by MIFs gave the cross-validated correlation coefficient, Q2 of 0.668 and squared correlation coefficient, R2 of 0.979 and the models by MLR, PCR and PLSR methods for 2D-QSAR provided a highly significant squared correlation coefficient (R2) values of 0.904, 0.785, 0.903 and cross-validated correlation coefficients (Q2) of 0.824, 0.662 and 0.718 respectively. The statistically significant model was established from a training set of 18 molecules, which were validated by evaluation of test set of 6 compounds. The calculated cytotoxic activities through MIFs model showed a very good agreement with experimental values. The information provided by QSAR analysis may give valuable clues to design and find the new potential drugs.
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A set of 24 halogen containing hydroxy and amino substituted aromatic compounds were subjected to 2D- and 3D-QSAR studies. 3D-QSAR was studied at a 2.0 Ǻ 3D grid spacing using molecular interaction fields (MIFs) analysis. The best predictive models by MIFs gave the cross-validated correlation coefficient, Q2 of 0.668 and squared correlation coefficient, R2 of 0.979 and the models by MLR, PCR and PLSR methods for 2D-QSAR provided a highly significant squared correlation coefficient (R2) values of 0.904, 0.785, 0.903 and cross-validated correlation coefficients (Q2) of 0.824, 0.662 and 0.718 respectively. The statistically significant model was established from a training set of 18 molecules, which were validated by evaluation of test set of 6 compounds. The calculated cytotoxic activities through MIFs model showed a very good agreement with experimental values. The information provided by QSAR analysis may give valuable clues to design and find the new potential drugs.
Key concepts: Quantitative structure–activity relationship, Correlation coefficient, Halogen, Chemistry, Test set, Coefficient of determination, Computational chemistry, Stereochemistry