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: | N6524 | |
|---|---|---|
| Name: | caffeine, 1-octyl-3-methylimidazolium hexafluorophosphate | |
| Description: | caffeine [MOIm]+[PF6]- | |
| Labels: | ||
| CAS: | ||
| InChi Code: | InChI=1S/C12H23N2.C8H10N4O2.F6P/c1-3-4-5-6-7-8-9-14-11-10-13(2)12-14;1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2;1-7(2,3,4,5)6/h10-12H,3-9H2,1-2H3;4H,1-3H3;/q+1;;-1 |
Properties
logK: Gas-ionic liquid partition coefficient
| Value | Source or prediction |
|---|---|
| 9.197 |
experimental value |
| 8.606935564585614 |
MLR: Multiple Linear Regression QSAR model for the gas-ionic liquid partition coefficient of organic solutes (MLR training predictions) |
| 9.401112357142853 |
RF: Random Forest Regression QSAR model for gas-ionic liquid partition coefficient of organic solutes (RF training predictions) |