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Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., William E.; Maran, U. The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids. J. Mol. Liq. 2021.

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Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., William E.; Maran, U. The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids. J. Mol. Liq. 2021.

QDB archive DOI: 10.15152/QDB.241   DOWNLOAD

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Property logK_BMPyrrFAP: Gas-ionic liquid partition coefficient of organic solvents in BMPyrrFAP

MLR1-Eq13: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of organic solvents in [BMPyrr]+[FAP]-

Regression model (regression)

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Name Type n

R2

σ

Training set training 80 0.943 0.274
RF1: Random Forest Regression QSAR model for gas-ionic liquid partition coefficient of organic solvents in [BMPyrr]+[FAP]-

Random forest (regression)

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Name Type n

R2

σ

Training set training 80 1.000 0.003

Property logK_MeoeMPyrrFAP: Gas-ionic liquid partition coefficient of organic solvents in MeoeMPyrrFAP

MLR2-Eq14: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of organic solvents in [MeoeMPyrr]+[FAP]-

Regression model (regression)

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Name Type n

R2

σ

Training set training 91 0.887 0.388
RF2: Random Forest Regression QSAR model for gas-ionic liquid partition coefficient of organic solvents in [MeoeMPyrr]+[FAP]-

Random forest (regression)

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Name Type n

R2

σ

Training set training 91 0.999 0.039

Property logK_BMPyrrC(CN)3: Gas-ionic liquid partition coefficient of organic solvents in BMPyrrC(CN)3

MLR3-Eq15: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of organic solvents in [BMPyrr]+[C(CN)3]-

Regression model (regression)

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Name Type n

R2

σ

Training set training 82 0.907 0.326
RF3: Random Forest Regression QSAR model for gas-ionic liquid partition coefficient of organic solvents in [BMPyrr]+[C(CN)3]-

Random forest (regression)

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Name Type n

R2

σ

Training set training 82 0.995 0.074

Citing

When using this QDB archive, please cite (see details) it together with the original article:

  • Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., W. E.; Maran, U. Data for: The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids. QsarDB repository, QDB.241. 2021. http://dx.doi.org/10.15152/QDB.241

  • Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., W. E.; Maran, U. The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids. J. Mol. Liq. 2021, http://dx.doi.org/10.1016/j.molliq.2021.117573

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dc.date.accessioned 2021-09-20T13:48:57Z
dc.date.available 2021-09-20T13:48:57Z
dc.date.issued 2021-09-20
dc.identifier.uri http://hdl.handle.net/10967/241
dc.identifier.uri http://dx.doi.org/10.15152/QDB.241
dc.description.abstract Ionic liquids (ILs) have unique properties as solvents and electrolytes, which need to be studied using innovative machine learning approaches and which allow the identification of a chemical environment that can be adapted to different applications. The gas-ionic liquid partition coefficients of organic compounds is one such application-oriented parameter for selecting both ionic liquids and organic compounds as quickly, cost-effectively, and as accurately as possible. Therefore, multiple linear regression (MLR) and random forest (RF) quantitative structure-property relationships (QSPRs) were developed for predicting the gas-ionic liquid partition coefficient (log K) of structurally variable organic solutes in the ionic liquids N-butyl-N-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([BMPyrr]+[FAP]–), N-butyl-N-methylpyrrolidinium tricyanomethanide ([BMPyrr]+[C(CN)3]–) and 1-​(2-​methoxyethyl)​-​1-​methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([MeoeMPyrr]+[FAP]–). All derived models have excellent prediction capability evidenced by high 5-fold cross-validated coefficients of determination in the range 0.88 – 0.94, complemented with other high statistical parameters. Compared to the MLR approach, the non-linear RF models statistics improved in two of three data series. Analysis of the molecular descriptors selected into MLR models revealed major solvent-solute interactions, with primary contributions from Coulomb and dipolar or hydrogen bonding interactions and followed by the descriptors that expose dispersion force related interactions. Relations to all the aforementioned solvent-solute interactions were also found in RF models descriptor interpretation. Comparison of models demonstrated that a common anion in different ILs produces a significant correlation between the data series log K values, while that of ILs with a common cation are less but still significantly correlated. The lower correlation could be attributed to varying structural differences in the corresponding ions, or the anion might have a more substantial role in determining partition properties with the organic solutes in the series. en_US
dc.publisher Karl Marti Toots
dc.publisher Sulev Sild
dc.publisher Jaan Leis
dc.publisher Acree Jr., William E.
dc.publisher Uko Maran
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.title Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., William E.; Maran, U. The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids. J. Mol. Liq. 2021.
qdb.property.endpoint 6. Other (Gas-ionic liquid partition coefficient) en_US
qdb.descriptor.application Mordred 1.1.1 en_US
qdb.prediction.application scikit-learn 0.24.1 en_US
bibtex.entry article en_US
bibtex.entry.author Toots, Karl Marti
bibtex.entry.author Sild, Sulev
bibtex.entry.author Leis, Jaan
bibtex.entry.author Acree Jr., William E.
bibtex.entry.author Maran, Uko
bibtex.entry.doi 10.1016/j.molliq.2021.117573
bibtex.entry.journal J. Mol. Liq. en_US
bibtex.entry.title The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids en_US
bibtex.entry.year 2021
qdb.model.type Regression model (regression) en_US
qdb.model.type Random forest (regression) en_US


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