Oja, M.; Sild, S.; Piir, G.; Maran, U. Intrinsic aqueous solubility: mechanistically transparent data-driven modeling of drug substances. Pharmaceutics 2022, 14, 2248.
Compound
| ID: | sc107 | |
|---|---|---|
| Name: | Chlorprothixene | |
| Description: | ||
| Labels: | ||
| CAS: | ||
| InChi Code: | InChI=1S/C18H18ClNS/c1-20(2)11-5-7-14-15-6-3-4-8-17(15)21-18-10-9-13(19)12-16(14)18/h3-4,6-10,12H,5,11H2,1-2H3/b14-7+ |
Properties
logS0: Intrinsic aqueous solubility from single source [log(mol/L)]
| Value | Source or prediction |
|---|---|
| -5.99 |
Avdeef, A. Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database. ADMET DMPK 2020, 8, 29–77. https://doi.org/http://doi.org/10.5599/admet.766 |
| -5.449 |
M1: Model with Dragon descriptors from training set 1 (Loose test set) |
| -5.449 |
M1: Model with Dragon descriptors from training set 1 (Test sets together) |
logS0a: Intrinsic aqueous solubility from multiple sources [log(mol/L)]
| Value | Source or prediction |
|---|---|
| -5.99 |
Avdeef, A. Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database. ADMET DMPK 2020, 8, 29–77. https://doi.org/http://doi.org/10.5599/admet.766 |
| -4.818 |
M2: Model with RDKit descriptors from training set 2 (Loose test set) |
| -4.818 |
M2: Model with RDKit descriptors from training set 2 (Test sets together) |
| -5.382 |
M3: Model with PaDEL and XLOGS descriptors from training set 2 (Loose test set) |
| -5.382 |
M3: Model with PaDEL and XLOGS descriptors from training set 2 (Test sets together) |
| -5.216 |
M_cons: Consensus model (average of predictions from M1, M2 and M3) (Loose test set) |
| -5.216 |
M_cons: Consensus model (average of predictions from M1, M2 and M3) (Test sets together) |