scholarly journals Singlet oxygen generation by porphyrins and metalloporphyrins revisited: A quantitative structure-property relationship (QSPR) study

2020 ◽  
Vol 403 ◽  
pp. 112833
Author(s):  
Andrey A. Buglak ◽  
Mikhail A. Filatov ◽  
M. Althaf Hussain ◽  
Manabu Sugimoto
Keyword(s):  
Singlet Oxygen ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Oxygen Generation ◽  
Singlet Oxygen Generation ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Qspr Study

A quantitative structure–property relationship (QSPR) study of singlet oxygen generation by pteridines

2016 ◽  
Vol 15 (6) ◽  
pp. 801-811 ◽  
Author(s):  
Andrey A. Buglak ◽  
Taisiya A. Telegina ◽  
Mikhail S. Kritsky
Keyword(s):  
Dipole Moment ◽  
Singlet Oxygen ◽  
Quantum Yields ◽  
Quantitative Structure ◽  
Oxygen Production ◽  
Structure Property ◽  
Oxygen Generation ◽  
Singlet Oxygen Generation ◽  
Structure Property Relationship ◽  
Qspr Study

Singlet oxygen production quantum yields of pteridine photosensitizers were analyzed with the QSPR method. The ability of pterins and flavins to generate1O2in D2O correlated withEHOMOand electronegativity, as well as with the dipole moment and some other parameters.

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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