Quantitative structure–property relationship studies on direct photolysis of selected polycyclic aromatic hydrocarbons in atmospheric aerosol

To realize the precise munipulation of the optoelectrical properties of boron–nitrogen (B–N) unit-doped Polycyclic aromatic hydrocarbons (PAHs), unraveling the structure-property relationship behind is of vital importance. In this work, we...

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Quantitative structure-property relationship (QSPR) study to predict retention time of polycyclic aromatic hydrocarbons using the random forest and artificial neural network methods

Structural Chemistry ◽

10.1007/s11224-019-01476-w ◽

2020 ◽

Vol 31 (4) ◽

pp. 1281-1288

Author(s):

Moona Emrarian ◽

Mahmoud Reza Sohrabi ◽

Nasser Goudarzi ◽

Fariba Tadayon

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Structure Property ◽

Network Methods ◽

Structure Property Relationship ◽

Polycyclic Aromatic ◽

Qspr Study

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Quantitative structure–property relationship models to predict thermodynamic properties of some mono and polycyclic aromatic hydrocarbons using genetic algorithm‐multiple linear regression

Journal of the Chinese Chemical Society ◽

10.1002/jccs.201900319 ◽

2019 ◽

Vol 67 (6) ◽

pp. 969-982

Author(s):

Fatemeh Dialamehpour ◽

Fatemeh Shafiei

Keyword(s):

Genetic Algorithm ◽

Polycyclic Aromatic Hydrocarbons ◽

Linear Regression ◽

Thermodynamic Properties ◽

Multiple Linear Regression ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Structure Property ◽

Structure Property Relationship ◽

Polycyclic Aromatic

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Prediction of solubility for polycyclic aromatic hydrocarbons in supercritical carbon dioxide using wavelet neural networks in quantitative structure property relationship

The Journal of Supercritical Fluids ◽

10.1016/j.supflu.2004.02.003 ◽

2004 ◽

Vol 32 (1-3) ◽

pp. 73-78 ◽

Cited By ~ 29

Author(s):

T. Khayamian ◽

M. Esteki

Keyword(s):

Carbon Dioxide ◽

Neural Networks ◽

Polycyclic Aromatic Hydrocarbons ◽

Supercritical Carbon Dioxide ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Structure Property ◽

Wavelet Neural Networks ◽

Structure Property Relationship ◽

Polycyclic Aromatic

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Quantitative structure–property relationship studies for direct photolysis rate constants and quantum yields of polybrominated diphenyl ethers in hexane and methanol

Ecotoxicology and Environmental Safety ◽

10.1016/j.ecoenv.2008.09.013 ◽

2009 ◽

Vol 72 (5) ◽

pp. 1587-1593 ◽

Cited By ~ 25

Author(s):

Lei Fang ◽

Jun Huang ◽

Gang Yu ◽

Xue Li

Keyword(s):

Rate Constants ◽

Polybrominated Diphenyl Ethers ◽

Quantum Yields ◽

Quantitative Structure ◽

Structure Property ◽

Direct Photolysis ◽

Quantitative Structure Property Relationship ◽

Property Relationship ◽

Structure Property Relationship ◽

Diphenyl Ethers

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Quantitative structure–property relationships for direct photolysis quantum yields of selected polycyclic aromatic hydrocarbons

The Science of The Total Environment ◽

10.1016/s0048-9697(99)00407-6 ◽

2000 ◽

Vol 246 (1) ◽

pp. 11-20 ◽

Cited By ~ 26

Author(s):

Jingwen Chen ◽

W.J.G.M. Peijnenburg ◽

Xie Quan ◽

Fenglin Yang

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Quantum Yields ◽

Quantitative Structure ◽

Structure Property ◽

Direct Photolysis ◽

Structure Property Relationships ◽

Quantitative Structure Property Relationships ◽

Polycyclic Aromatic

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

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207321666180927102836 ◽

2018 ◽

Vol 21 (7) ◽

pp. 533-542 ◽

Cited By ~ 2

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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