Rapid QSPR model development technique for prediction of vapor pressure of organic compounds

Katritzky, Alan R.; Slavov, Svetoslav; Dobchev, Dimitar A.; Karelson, Mati

doi:10.1016/j.compchemeng.2006.10.001

Cited by 54 publications

(53 citation statements)

References 23 publications

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“…4, the residuals exceed seldom the standard deviation of ±2S. Therefore, no systematic characteristics exist in the errors, which is in agreement with the general multiple linear theory [20]. The absence of data clustering suggests that our quantitative structure property relationship model is satisfactory.…”

Section: Regression Analysissupporting

confidence: 78%

Estimation of surface tension of organic compounds using quantitative structure-property relationship

Yimin

Liu

et al. 2012

J. Cent. South Univ. Technol.

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A novel quantitative structure−property relationship (QSPR) model for estimating the solution surface tension of 92 organic compounds at 20 °C was developed based on newly introduced atom-type topological indices. The data set contained non-polar and polar liquids, and saturated and unsaturated compounds. The regression analysis shows that excellent result is obtained with multiple linear regression. The predictive power of the proposed model was discussed using the leave-one-out (LOO) cross-validated (CV) method. The correlation coefficient (R) and the leave-one-out cross-validation correlation coefficient (R CV ) of multiple linear regression model are 0.991 4 and 0.991 3, respectively. The new model gives the average absolute relative deviation of 1.81% for 92 substances. The result demonstrates that novel topological indices based on the equilibrium electro-negativity of atom and the relative bond length are useful model parameters for QSPR analysis of compounds.

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Section: Regression Analysissupporting

confidence: 78%

Estimation of surface tension of organic compounds using quantitative structure-property relationship

Yimin

Liu

et al. 2012

J. Cent. South Univ. Technol.

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“…The pertinence of the DFT level has been checked for such systems in ref [20, 22]. AM1 is the most popular semi‐empirical level for this kind of studies [39–42]. Structure optimization and harmonic frequency calculations have been performed at both levels and all structures have been checked to present no imaginary frequency, ensuring that all stationary points are stable species.…”

Section: Methodsmentioning

confidence: 99%

Predicting explosibility properties of chemicals from quantitative structure‐property relationships

Fayet

Rotureau

Joubert

et al. 2010

Process Safety Progress

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“…In our context, we apply ANN to predict a property (EVPIE) by using quantitative information on the structure (molecular descriptors). In this case, it is a Quantitative Structure Property Relationship (QSPR, Katritzky et al, 2007).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression – A quantitative structure property relationship approach

et al. 2015

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Rapid QSPR model development technique for prediction of vapor pressure of organic compounds

Cited by 54 publications

References 23 publications

Estimation of surface tension of organic compounds using quantitative structure-property relationship

Estimation of surface tension of organic compounds using quantitative structure-property relationship

Predicting explosibility properties of chemicals from quantitative structure‐property relationships

Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression – A quantitative structure property relationship approach

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