10967/264 - QDB Compounds

QsarDB Repository

Belfield, S. J.; Cronin, M. T. D.; Enoch, S. J.; Firman, J. W. Guidance for Good Practice in the Application of Machine Learning in Development of Toxicological Quantitative Structure-Activity Relationships (QSARs). PLOS ONE, 2023, 18, e0282924.

Compound

ID:209
Name:2-methylpropan-1-amine
Description:
Labels:Neutral
CAS:78-81-9
InChi Code:InChI=1/C4H11N/c1-4(2)3-5/h4H,3,5H2,1-2H3

Properties

pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)] i

ValueSource or prediction
-0.26

experimental value

-0.43

RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set)

-0.81

RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation)

-0.45

SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set)

-0.72

SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation)

-0.81

KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set)

-0.96

KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation)

-0.4

XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set)

-0.69

XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation)

-0.5

SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set)

-0.57

SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation)

-0.24

DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set)

-0.65

DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation)