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: | t22 | |
|---|---|---|
| Name: | Meclizine | |
| Description: | ||
| Labels: | ||
| CAS: | 569-65-3 | |
| InChi Code: | InChI=1/C25H27ClN2/c1-20-6-5-7-21(18-20)19-27-14-16-28(17-15-27)25(22-8-3-2-4-9-22)23-10-12-24(26)13-11-23/h2-13,18,25H,14-17,19H2,1H3 |
Properties
logS0: Intrinsic aqueous solubility from single source [log(mol/L)]
| Value | Source or prediction |
|---|---|
| -6.48 |
Avdeef, A. Multi-lab intrinsic solubility measurement reproducibility in CheqSol and shake-flask methods. ADMET DMPK 2019, 7, 210–219. https://doi.org/http://doi.org/10.5599/admet.698 |
| -6.791 |
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 |
|---|---|
| -6.48 |
experimental value |
| -5.184 |
M2: Model with RDKit descriptors from training set 2 (Training set) |
| -4.882 |
M3: Model with PaDEL and XLOGS descriptors from training set 2 (Training set) |
| -5.619 |
M_cons: Consensus model (average of predictions from M1, M2 and M3) (Training set) |