Application of QSPR for the prediction of gas to 1-octanol solvation enthalpy using support vector regression

Golmohammadi, Hassan; Dashtbozorgi, Zahra; Acree, William E.

doi:10.1080/00319104.2012.708932

Cited by 9 publications

(5 citation statements)

References 46 publications

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“…The reported correlation coefficients for these models [31,54] are similar to, or greater than what we report for Model 2 and the standard errors of the models developed using a support vector machine method and artificial neural networks is smaller than the RMSE of Model 2. However, these models cannot be used easily in high throughput applications because acquiring the molecular descriptors requires multiple software applications [54], which can be time-consuming and lends itself to increased chances of human error.…”

Section: Comparison With Other 1u • Oa Modelssupporting

confidence: 75%

“…If we compare the performance of the Mintz et al [19] model and Model 2 when using all 195 chemicals in the ΔU • OA dataset, we see that Model 2 performs better than the Mintz et al [19] model (Table 4). While there is little difference between the statistics on the residuals, fewer chemicals have residuals greater than 10 kJ•mol −1 when using Model In addition to the model by Mintz et al [19], there is a ppLFER for wet-octanol air ΔH • values [31] and models for ΔH • OA using a support vector machine method, artificial neural networks, and MLRs based on various molecular descriptors [54]. The wet-octanol air ΔH • OA model is based on the same dataset used to develop the Mintz et al [19] model above [31].…”

Section: Comparison With Other 1u • Oa Modelsmentioning

confidence: 99%

“…The wet-octanol air ΔH • OA model is based on the same dataset used to develop the Mintz et al [19] model above [31]. The latter three models also use the same dataset as a basis for model development but randomly split their dataset of 127 chemicals into a training dataset of 89 chemicals and validation dataset of 38 chemicals [54]. The reported correlation coefficients for these models [31,54] are similar to, or greater than what we report for Model 2 and the standard errors of the models developed using a support vector machine method and artificial neural networks is smaller than the RMSE of Model 2.…”

Section: Comparison With Other 1u • Oa Modelsmentioning

confidence: 99%

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Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$

et al. 2022

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The octanol–air partition ratio (KOA) describes the partitioning of a chemical between air and octanol and is often used to approximate other partitioning phenomena in environmental chemistry (e.g., blood–air, atmospheric particulate matter–air, polyurethane foam-air). Such partitioning processes often occur at environmental temperatures other than 25 °C. Enthalpies $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ or internal energies $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of phase transfer are used to express the temperature dependence of the KOA. Existing poly-parameter linear free energy relationships (ppLFERs) for predicting $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ were developed using a relatively small dataset. In this work we utilize a recently developed comprehensive KOA database to create and curate a $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ dataset containing 195 chemicals and use this dataset in the development of new predictive equations. Using the QSAR development platform QSARINS we evaluate the use of Abraham descriptors, other molecular descriptors, and the log10KOA at 25 °C as variables in different multilinear regression equations for $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ . The $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of neutral organic chemicals can be reliably predicted using only the log10KOA (RMSEEXT = 6.86 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2 = 0.94), only the solute’s hydrogen acidity A and the logarithm of the hexadecane–air partition ratio L (RMSEEXT = 7.23 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2 = 0.93), or A and log10KOA (RMSEEXT = 6.76 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2 = 0.95).

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Section: Comparison With Other 1u • Oa Modelssupporting

confidence: 75%

Section: Comparison With Other 1u • Oa Modelsmentioning

confidence: 99%

Section: Comparison With Other 1u • Oa Modelsmentioning

confidence: 99%

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Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$

et al. 2022

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“…Support vector machine (SVM) is a novel kind of machine learning method, and is gaining reputation owing to many attractive features as well as talented empirical performance. SVMs have found numerous applications in prediction in QSPR studies [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Modeling and predicting the solute polarity parameter in reversed‐phase liquid chromatography using quantitative structure–property relationship approaches

Golmohammadi

Dashtbozorgi

Khooshechin

2017

J of Separation Science

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A prediction of quantitative structure-property relationships is developed to model the polarity parameter of a set of 146 organic compounds in acetonitrile in reversed-phase liquid chromatography. Enhanced replacement method and support vector machine regressions were employed to build prediction models based on molecular descriptors calculated from the structure alone. The correlation coefficients between experimental and predicted values of polarity parameter for the test set by enhanced replacement method and support vector machine were 0.970 and 0.993, respectively. The obtained results demonstrated that the support vector machine model is more reliable and has a better prediction performance than the enhanced replacement method.

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“…Due to its wonderful generalization performance, the SVM has attracted interest and obtained extensive application [13][14][15][16][17][18][19] . In recent years, SVM has also exposed enormous performance in QSPR studies owing to its ability to construe the nonlinear relationships between molecular structure and properties [20][21][22][23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

Golmohammadi¹,

Dashtbozorgi²,

Khooshechin³

2017

Acta Physico-Chimica Sinica

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The purpose of this paper is to present a novel way to building quantitative structure-property relationship (QSPR) models for predicting the gas-to-benzene solvation enthalpy (ΔHSolv) of 158 organic compounds based on molecular descriptors calculated from the structure alone. Different kinds of descriptors were calculated for each compounds using dragon package. The variable selection technique of enhanced replacement method (ERM) was employed to select optimal subset of descriptors. Our investigation reveals that the dependence of physico-chemical properties on solvation enthalpy is a nonlinear observable fact and that ERM method is unable to model the solvation enthalpy accurately. The standard error value of prediction set for support vector machine (SVM) is 1.681 kJ • mol-1 while it is 4.624 kJ • mol-1 for ERM. The results established that the calculated ΔHSolv values by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by ERM one. This indicates that SVM can be used as an alternative modeling tool for QSPR studies.

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Application of QSPR for the prediction of gas to 1-octanol solvation enthalpy using support vector regression

Cited by 9 publications

References 46 publications

Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$

Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$

Modeling and predicting the solute polarity parameter in reversed‐phase liquid chromatography using quantitative structure–property relationship approaches

Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

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