10967/257 - QDB Compounds

QsarDB Repository

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:sc37
Name:Diphenylamine
Description:
Labels:
CAS:
InChi Code:InChI=1S/C12H11N/c1-3-7-11(8-4-1)13-12-9-5-2-6-10-12/h1-10,13H

Properties

logS0: Intrinsic aqueous solubility from single source [log(mol/L)]

ValueSource or prediction
-3.53

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. http://dx.doi.org/http://doi.org/10.5599/admet.766

-3.077

M1: Model with Dragon descriptors from training set 1 (Tight test set)

-3.077

M1: Model with Dragon descriptors from training set 1 (Test sets together)

logS0a: Intrinsic aqueous solubility from multiple sources [log(mol/L)]

ValueSource or prediction
-3.53

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. http://dx.doi.org/http://doi.org/10.5599/admet.766

-3.472

M2: Model with RDKit descriptors from training set 2 (Tight test set)

-3.472

M2: Model with RDKit descriptors from training set 2 (Test sets together)

-3.541

M3: Model with PaDEL and XLOGS descriptors from training set 2 (Tight test set)

-3.541

M3: Model with PaDEL and XLOGS descriptors from training set 2 (Test sets together)

-3.363

M_cons: Consensus model (average of predictions from M1, M2 and M3) (Tight test set)

-3.363

M_cons: Consensus model (average of predictions from M1, M2 and M3) (Test sets together)