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: | t81 | |
|---|---|---|
| Name: | Dipyridamole | |
| Description: | ||
| Labels: | ||
| CAS: | 58-32-2 | |
| InChi Code: | InChI=1S/C24H40N8O4/c33-15-11-31(12-16-34)23-26-20-19(21(27-23)29-7-3-1-4-8-29)25-24(32(13-17-35)14-18-36)28-22(20)30-9-5-2-6-10-30/h33-36H,1-18H2 |
Properties
logS0: Intrinsic aqueous solubility from single source [log(mol/L)]
| Value | Source or prediction |
|---|---|
| -5.16 |
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 |
| -3.994 |
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 |
|---|---|
| -5.16 |
experimental value |
| -2.761 |
M2: Model with RDKit descriptors from training set 2 (Training set) |
| -4.739 |
M3: Model with PaDEL and XLOGS descriptors from training set 2 (Training set) |
| -3.831 |
M_cons: Consensus model (average of predictions from M1, M2 and M3) (Training set) |