Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E.; Maran, U. A multicomponent QSPR approach to describe and predict gas-ionic liquid distribution of organic solutes using machine learning. J. Mol. Liq. 2025, 436, 128184.
Compound
| ID: | N6527 | |
|---|---|---|
| Name: | caffeine, 1-butyl-3-methylimidazolium hexafluorophosphate | |
| Description: | caffeine [MBIm]+[PF6]- | |
| Labels: | ||
| CAS: | ||
| InChi Code: | InChI=1S/C8H10N4O2.C8H15N2.F6P/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2;1-3-4-5-10-7-6-9(2)8-10;1-7(2,3,4,5)6/h4H,1-3H3;6-8H,3-5H2,1-2H3;/q;+1;-1 |
Properties
logK: Gas-ionic liquid partition coefficient
| Value | Source or prediction |
|---|---|
| 9.746 |
experimental value |
| 8.490546039709407 |
MLR: Multiple Linear Regression QSAR model for the gas-ionic liquid partition coefficient of organic solutes (MLR training predictions) |
| 9.707299357142851 |
RF: Random Forest Regression QSAR model for gas-ionic liquid partition coefficient of organic solutes (RF training predictions) |