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:10
Name:2-chloroacetonitrile
Description:
Labels:Neutral
CAS:107-14-2
InChi Code:InChI=1/C2H2ClN/c3-1-2-4/h1H2

Properties

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

ValueSource or prediction
0.85

experimental value

0.32

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

-0.21

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

0.84

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

0.33

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

1.35

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

0.31

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

0.87

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

0.56

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

0.71

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

0.68

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

0.93

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

0.41

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