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: | t58 | |
|---|---|---|
| Name: | Prochlorperazine | |
| Description: | ||
| Labels: | ||
| CAS: | 58-38-8 | |
| InChi Code: | InChI=1S/C20H24ClN3S/c1-22-11-13-23(14-12-22)9-4-10-24-17-5-2-3-6-19(17)25-20-8-7-16(21)15-18(20)24/h2-3,5-8,15H,4,9-14H2,1H3 |
Properties
logS0: Intrinsic aqueous solubility from single source [log(mol/L)]
| Value | Source or prediction |
|---|---|
| -4.75 |
Avdeef, A. Multi-lab intrinsic solubility measurement reproducibility in CheqSol and shake-flask methods. ADMET DMPK 2019, 7, 210–219. http://dx.doi.org/http://doi.org/10.5599/admet.698 |
| -5.015 |
M1: Model with Dragon descriptors from training set 1 (Training set) |
logS0a: Intrinsic aqueous solubility from multiple sources [log(mol/L)]
| Value | Source or prediction |
|---|---|
| -4.4 |
experimental value |
| -4.41 |
M2: Model with RDKit descriptors from training set 2 (Training set) |
| -4.413 |
M3: Model with PaDEL and XLOGS descriptors from training set 2 (Training set) |
| -4.613 |
M_cons: Consensus model (average of predictions from M1, M2 and M3) (Training set) |