Binding of Arylpiperazines, (Aryloxy)propanolamines, and Tetrahydropyridylindoles to the 5-HT1AReceptor:  Contribution of the Molecular Lipophilicity Potential to Three-Dimensional Quantitative Structure−Affinity Relationship Models

1996 ◽  
Vol 39 (1) ◽  
pp. 126-134 ◽  
Author(s):  
Patrick Gaillard ◽  
Pierre-Alain Carrupt ◽  
Bernard Testa ◽  
Philippe Schambel
Keyword(s):  
Three Dimensional ◽  
Quantitative Structure ◽  
Lipophilicity Potential ◽  
Molecular Lipophilicity Potential

scholarly journals Molecular Shape Descriptors. 2. Quantitative Structure-Activity Relationships Based Upon Three-Dimensional Molecular Shape Descriptor

1985 ◽  
Vol 40 (11) ◽  
pp. 1114-1120
Author(s):  
loan Motoc ◽  
Garland R. Marshall
Keyword(s):  
Enzyme Inhibitor ◽  
Three Dimensional ◽  
Shape Descriptor ◽  
Molecular Shape ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
E Coli ◽  
Interactional Pattern ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

A methodology to incorporate the three-dimensional molecular shape descriptor (3 D-MSD) into a quantitative structure-activity relationship is discussed in detail. The 3 D-MSD is calculated and correlated with Kiapp values for a set of 2,4-diamino-5-benzylpyrimidines which inhibit E. coli DHFR. The correlation (n = 22, r = 0.95, s = 0.214, F = 55.10) indicates that the polarization interaction dominates the enzyme-inhibitor interactional pattern.

Antileishmanial Chalcones:  Statistical Design, Synthesis, and Three-Dimensional Quantitative Structure−Activity Relationship Analysis

1998 ◽  
Vol 41 (24) ◽  
pp. 4819-4832 ◽  
Author(s):  
Simon Feldbæk Nielsen ◽  
Søren Brøgger Christensen ◽  
Gabriele Cruciani ◽  
Arsalan Kharazmi ◽  
Tommy Liljefors
Keyword(s):  
Three Dimensional ◽  
Statistical Design ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Design Synthesis ◽  
Relationship Analysis ◽  
Structure Activity

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

2021 ◽  
Vol 16 (10) ◽  
pp. 50-58
Author(s):  
Ali Qusay Khalid ◽  
Vasudeva Rao Avupati ◽  
Husniza Hussain ◽  
Tabarek Najeeb Zaidan
Keyword(s):  
Dengue Fever ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Contour Maps ◽  
Structure Activity ◽  
Bioactive Ligands

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.

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