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: | sc28 | |
|---|---|---|
| Name: | Cyclosporine A | |
| Description: | ||
| Labels: | ||
| CAS: | ||
| InChi Code: | InChI=1S/C62H111N11O12/c1-25-27-28-40(15)52(75)51-56(79)65-43(26-2)58(81)67(18)33-48(74)68(19)44(29-34(3)4)55(78)66-49(38(11)12)61(84)69(20)45(30-35(5)6)54(77)63-41(16)53(76)64-42(17)57(80)70(21)46(31-36(7)8)59(82)71(22)47(32-37(9)10)60(83)72(23)50(39(13)14)62(85)73(51)24/h25,27,34-47,49-52,75H,26,28-33H2,1-24H3,(H,63,77)(H,64,76)(H,65,79)(H,66,78)/b27-25+/t40-,41+,42-,43+,44+,45+,46+,47+,49+,50+,51+,52-/m1/s1 |
Properties
logS0: Intrinsic aqueous solubility from single source [log(mol/L)]
| Value | Source or prediction |
|---|---|
| -5.03 |
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 |
| -6.963 |
M1: Model with Dragon descriptors from training set 1 (Tight test set) |
| -6.963 |
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 |
|---|---|
| -5.03 |
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 |
| -7.769 |
M2: Model with RDKit descriptors from training set 2 (Tight test set) |
| -7.769 |
M2: Model with RDKit descriptors from training set 2 (Test sets together) |
| -10.087 |
M3: Model with PaDEL and XLOGS descriptors from training set 2 (Tight test set) |
| -10.087 |
M3: Model with PaDEL and XLOGS descriptors from training set 2 (Test sets together) |
| -8.273 |
M_cons: Consensus model (average of predictions from M1, M2 and M3) (Tight test set) |
| -8.273 |
M_cons: Consensus model (average of predictions from M1, M2 and M3) (Test sets together) |