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 ◽

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Comparison of multiple linear regression, partial least squares and artificial neural network for quantitative structure retention relationships of some polycyclic aromatic hydrocarbons

Analytical Methods ◽

10.1039/c2ay25711d ◽

2012 ◽

Vol 4 (10) ◽

pp. 3381 ◽

Author(s):

Mohamad Mahani ◽

Homa ShaikhGhomi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Polycyclic Aromatic Hydrocarbons ◽

Linear Regression ◽

Multiple Linear Regression ◽

Least Squares ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Polycyclic Aromatic ◽

Structure Retention

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Comparison of Genetic Algorithm Based Support Vector Machine and Genetic Algorithm Based RBF Neural Network in Quantitative Structure-Property Relationship Models on Aqueous Solubility of Polycyclic Aromatic Hydrocarbons

Procedia Environmental Sciences ◽

10.1016/j.proenv.2010.10.156 ◽

2010 ◽

Vol 2 ◽

pp. 1429-1437 ◽

Author(s):

Qi Jun ◽

Sun Chang-Hong ◽

Wei Jia

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Aromatic Hydrocarbons ◽

Rbf Neural Network ◽

Aqueous Solubility ◽

Support Vector ◽

Quantitative Structure ◽

Structure Property ◽

Structure Property Relationship ◽

Polycyclic Aromatic

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Quantitative Structure-Property Relationship: XXII. Polycyclic Aromatic Hydrocarbons

Russian Journal of General Chemistry ◽

10.1007/s11176-005-0511-8 ◽

2005 ◽

Vol 75 (11) ◽

pp. 1790-1794

Author(s):

I. B. Golovanov ◽

S. M. Zhenodarova

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Structure Property ◽

Quantitative Structure Property Relationship ◽

Property Relationship ◽

Structure Property Relationship ◽

Polycyclic Aromatic

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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 ◽

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 on direct photolysis of selected polycyclic aromatic hydrocarbons in atmospheric aerosol

Chemosphere ◽

10.1016/s0045-6535(00)00077-1 ◽

2001 ◽

Vol 42 (3) ◽

pp. 263-270 ◽

Author(s):

Jingwen Chen ◽

Xie Quan ◽

Yun Yan ◽

Fenglin Yang ◽

W.J.G.M Peijnenburg

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Atmospheric Aerosol ◽

Quantitative Structure ◽

Structure Property ◽

Direct Photolysis ◽

Quantitative Structure Property Relationship ◽

Property Relationship ◽

Structure Property Relationship ◽

Polycyclic Aromatic

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Quantitative structure–property relationship studies of gas-to-wet butyl acetate partition coefficient of some organic compounds using genetic algorithm and artificial neural network

Structural Chemistry ◽

10.1007/s11224-010-9669-8 ◽

2010 ◽

Vol 21 (6) ◽

pp. 1241-1252 ◽

Author(s):

Hassan Golmohammadi ◽

Zahra Dashtbozorgi

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Partition Coefficient ◽

Organic Compounds ◽

Butyl Acetate ◽

Quantitative Structure ◽

Structure Property ◽

Quantitative Structure Property Relationship ◽

Structure Property Relationship

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Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons

Talanta ◽

10.1016/j.talanta.2008.02.004 ◽

2008 ◽

Vol 76 (1) ◽

pp. 66-71 ◽

Author(s):

Snežana Sremac ◽

Aleksandar Popović ◽

Žaklina Todorović ◽

Đuro Čokeša ◽

Antonije Onjia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Chromatographic Separation ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Polycyclic Aromatic ◽

Gas Chromatographic Separation

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Quantitative structure-property relationship modeling of water-to-wet butyl acetate partition coefficient of 76 organic solutes using multiple linear regression and artificial neural network

Journal of Separation Science ◽

10.1002/jssc.201000448 ◽

2010 ◽

Vol 33 (23-24) ◽

pp. 3800-3810 ◽

Author(s):

Zahra Dashtbozorgi ◽

Hassan Golmohammadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Partition Coefficient ◽

Multiple Linear Regression ◽

Butyl Acetate ◽

Organic Solutes ◽

Quantitative Structure ◽

Structure Property ◽

Structure Property Relationship

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Aggregation Effects on Photophysical Properties of NBN-Doped Polycyclic Aromatic Hydrocarbons: A Theoretical Study

Physical Chemistry Chemical Physics ◽

10.1039/d1cp03726a ◽

2021 ◽

Author(s):

Yi Zeng ◽

Junfang Yang ◽

Xiaoyan Zheng

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Theoretical Study ◽

Photophysical Properties ◽

Structure Property ◽

Vital Importance ◽

Property Relationship ◽

Structure Property Relationship ◽

Polycyclic Aromatic ◽

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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