A Short Review of the Generation of Molecular Descriptors and Their Applications in Quantitative Structure Property/Activity Relationships

2016 ◽  
Vol 12 (3) ◽  
pp. 181-205 ◽  
Author(s):  
Sagarika Sahoo ◽  
Chandana Adhikari ◽  
Minati Kuanar ◽  
Bijay Mishra
Keyword(s):  
Molecular Descriptors ◽  
Short Review ◽  
Quantitative Structure ◽  
Structure Property

Prediction of the Standard Enthalpy of Formation of Pure Compounds Using Molecular Structure

2009 ◽  
Vol 62 (4) ◽  
pp. 376 ◽  
Author(s):  
Farhad Gharagheizi
Keyword(s):  
Enthalpy Of Formation ◽  
Standard Enthalpy ◽  
Molecular Descriptors ◽  
Standard Enthalpy Of Formation ◽  
Quantitative Structure ◽  
Structure Property ◽  
Feed Forward Neural Network ◽  
Structure Property Relationship ◽  
Predictive Approach ◽  
Pure Compounds

A predictive approach has been presented to calculate the standard enthalpy of formation of pure compounds based on a quantitative structure–property relationship technique. A large number (1692) of pure compounds were used in this study. A genetic algorithm based on multivariate linear regression was used to subset variable selection. Using the selected molecular descriptors an optimized feed forward neural network was presented to predict the ΔHfo of pure compounds.

scholarly journals A New Accurate Neural Network Quantitative Structure- Property Relationship for Prediction of θ (Lower Critical Solution Temperature) of Polymer Solutions

e-Polymers ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Farhad Gharagheizi
Keyword(s):  
Neural Network ◽  
Molecular Descriptors ◽  
Polymer Solutions ◽  
Solution Temperature ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Relationship Model

Abstract In this study, a new neural network quantitative structure-property relationship model for prediction of θ (LCST ) of polymer solutions is presented. The parameters of this model are eight molecular descriptors which are calculated only from the chemical structure of polymer and solvent. These eight molecular descriptors were selected from 3328 molecular descriptors of polymer and solvent available in polymer solution by genetic algorithm-based multivariate linear regression (GA-MLR) technique. The obtained neural network model can predict the θ (LCST ) of 169 polymer solutions with mean relative error of 1.67% and squared correlation coefficient of 0.9736.

scholarly journals Quantitative Structure-Property Study on Pyrazines with Bell Pepper Flavor

2000 ◽  
Vol 68 (1) ◽  
pp. 41-56
Author(s):  
Ch. Th. Klein ◽  
H. Pircher ◽  
B. Wailzer ◽  
G. Buchbauer ◽  
P. Wolschann
Keyword(s):  
Biological Activity ◽  
Statistical Methods ◽  
Molecular Descriptors ◽  
Molecular Properties ◽  
Bell Pepper ◽  
Quantitative Structure ◽  
Structure Property ◽  
Qspr Study

A quantitative structure-property (QSPR) study on pyrazines with bell pepper aroma is performed by means of different statistical methods, which correlate appropriate molecular descriptors with the biological activity. The different methods lead to consistent results, indicating which of the molecular properties of the compounds under consideration are significant for bell pepper flavor. These results are compared with other models.

Predicting H2S solubility in ionic liquids by the quantitative structure–property relationship method using Sσ-profile molecular descriptors

RSC Advances ◽  
2016 ◽  
Vol 6 (74) ◽  
pp. 70405-70413 ◽  
Author(s):  
Yongsheng Zhao ◽  
Jubao Gao ◽  
Ying Huang ◽  
Raja Muhammad Afzal ◽  
Xiangping Zhang ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Hydrogen Sulfide ◽  
Molecular Descriptors ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship

Predicting hydrogen sulfide (H2S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization.

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