Exploring Quantum Chemical Descriptors and Molecular Docking Approach for Designing Antagonist Search Model for the Glycine/NMDA Receptor Site

2017 ◽  
Vol 2 (32) ◽  
pp. 10476-10483
Author(s):  
Snehal V. Ingle ◽  
Kaustubh A. Joshi
Keyword(s):  
Molecular Docking ◽  
Nmda Receptor ◽  
Quantum Chemical ◽  
Receptor Site ◽  
Search Model ◽  
Quantum Chemical Descriptors ◽  
Docking Approach ◽  
Chemical Descriptors

Reactivity of fullerenes. Quantum-chemical descriptors versus curvature

1995 ◽  
Vol 338 (1-3) ◽  
pp. 293-301 ◽  
Author(s):  
K. Choho ◽  
W. Langenaeker ◽  
G. Van De Woude ◽  
P. Geerlings
Keyword(s):  
Quantum Chemical ◽  
Quantum Chemical Descriptors ◽  
Chemical Descriptors

scholarly journals A QUANTUM-CHEMICAL DESCRIPTORS OF CARBONIC ANHYDRASE CA(I) INHIBITION BY AROMATIC SULFONYL AMIDES

2020 ◽  
Vol 2 (29(56)) ◽  
pp. 27-30
Author(s):  
E.N. Krylov ◽  
L.V. Virzum ◽  
I.D. Kryukova
Keyword(s):  
Nitrogen Atom ◽  
Carbonic Anhydrase ◽  
Electrostatic Potential ◽  
Quantum Chemical ◽  
Molecular Electrostatic Potential ◽  
Quantum Chemical Descriptors ◽  
Carbonic Anhydrase Inhibition ◽  
Chemical Descriptors ◽  
Quantum Parameters

The quantum parameters of aromatic sulfonylamides are determined to describe their interaction with carbonic anhydrase at the theory level M06/6-311 ++G**(SMD). The molecular electrostatic potential on the nitrogen atom in sulfamides and Hirschfeld charge on this atom have been found to appear to be adequate and determinative descriptors of carbonic anhydrase inhibition.

scholarly journals Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

2008 ◽  
Vol 6 (2) ◽  
pp. 310-318 ◽  
Author(s):  
Gui-Ning Lu ◽  
Xue-Qin Tao ◽  
Zhi Dang ◽  
Xiao-Yun Yi ◽  
Chen Yang
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Partition Coefficients ◽  
Density Functional ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

AbstractQuantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

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