Showing papers by "Bareilly College published in 2005"
TL;DR: Four different methods have been employed to certify the reliability of QSAR study of 50 phenol derivatives and the DFT models have a higher predictive power than AM1, PM3, and PM5 methods.
61 citations
TL;DR: The predicted values of biological activity with the help of multiple linear regression (MLR) analysis is very close to observed biological activity and the cross-validation coefficient and correlation coefficient indicate that the QSAR model is valuable.
Abstract: Ionization potential of an atom in a molecule, electron affinity of an atom in a molecule, and quantum chemical descriptor atomic softness values En‡-based quantitative structure–activity relationship (QSAR) study of testosterone derivatives have been done with the help of PM3 calculations on WinMOPAC 7.21 software. The 3D modeling and geometry optimization of all the compounds have been done with the help of PCMODEL software. The biological activities of testosterone derivatives have been taken from literature. The predicted values of biological activity with the help of multiple linear regression (MLR) analysis is very close to observed biological activity. The cross-validation coefficient and correlation coefficient also indicate that the QSAR model is valuable. Regression analysis shows a very good relationship with biological activity and En‡ values. With the help of these values, prediction of the biological activity of any unknown compound is possible. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2005
41 citations
TL;DR: Regression models indicate that absolute hardness in combination with different energy descriptors provide better correlation between observed relative binding affinity (RBA) and predicted relativebinding affinity (PA) in the case of estrogen derivatives.
Abstract: Quantum chemical descriptors (∈ H O M O , ∈ L U M O , absolute hardness, global softness, chemical potential, and electronegativity) and energy descriptors (Q m i n , ΔH 0 f , E T , and E E ) based QSAR study of estrogen derivatives was made with the help of PM3 calculations on WinMOPAC 7.21 software. The observed RBA values of estrogens were taken from the literature. QSAR models were made using different quantum chemical and energy descriptors with the help of multiple linear regression analysis. Regression models indicate that absolute hardness in combination with different energy descriptors provide better correlation between observed relative binding affinity (RBA) and predicted relative binding affinity (PA). Regression models for other quantum chemical descriptors with energy descriptors are not as clear as in the case of absolute hardness. Hardness provides a better picture due to the maximum hardness principle and can be used as a QSAR model for predicting the biological activity of any compound.
40 citations
TL;DR: The QSAR study shows that, Δ Enm† values provide good relationship with biological activity and the required parameters for the solution of Klopman equation have been calculated with the help of PM3 method.
Abstract: Softness values E
n
† of estrogen derivatives and softness values E
m
† of receptor lysine, histidine, tyrosine and cysteine have been evaluated by Klopman equation. The required parameters for the solution of Klopman equation have been calculated with the help of PM3 method. The difference Δ E
nm
† between E
n
† and E
m
† has been derived for QSAR study. The estrogen derivatives have been divided into four different sets on the basis of their structural similarities, and their biological activity taken from literature in terms of relative binding affinity (RBA). The QSAR study shows that, Δ E
nm
† values provide good relationship with biological activity.
22 citations