Quantitative Structure–Property Relationship (QSPR) Modeling of Normal Boiling Point Temperature and Composition of Binary Azeotropes

2011 ◽  
Vol 50 (24) ◽  
pp. 14162-14167 ◽  
Author(s):  
Vitaly P. Solov’ev ◽  
Ioana Oprisiu ◽  
Gilles Marcou ◽  
Alexandre Varnek
Keyword(s):  
Boiling Point ◽  
Normal Boiling Point ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Point Temperature ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Qspr Modeling

Predictive quantitative structure–property relationship (QSPR) modeling for adsorption of organic pollutants by carbon nanotubes (CNTs)

2019 ◽  
Vol 6 (1) ◽  
pp. 224-247 ◽  
Author(s):  
Joyita Roy ◽  
Sulekha Ghosh ◽  
Probir Kumar Ojha ◽  
Kunal Roy
Keyword(s):  
Carbon Nanotubes ◽  
Organic Pollutants ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Qspr Modeling ◽  
New Generation ◽  
Inorganic And Organic

Nanotechnology has introduced a new generation of adsorbents like carbon nanotubes (CNTs), which have drawn a widespread attention due to their outstanding ability for the removal of various inorganic and organic pollutants.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

2018 ◽  
Vol 21 (7) ◽  
pp. 533-542 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei ◽  
Tahereh Momeni Isfahani
Keyword(s):  
Thermodynamic Properties ◽  
Predictive Ability ◽  
Total Sample ◽  
Basis Sets ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Chemical Structures ◽  
Property Relationship ◽  
Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

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