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:1926
Name:butyl (2E)-3-phenylprop-2-enoate
Description:
Labels:Neutral
CAS:538-65-8
InChi Code:InChI=1/C13H16O2/c1-2-3-11-15-13(14)10-9-12-7-5-4-6-8-12/h4-10H,2-3,11H2,1H3/b10-9+

Properties

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

ValueSource or prediction
1.53

experimental value

1.45

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

1.26

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

1.52

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

1.51

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

1.57

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

1.51

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

1.51

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

1.38

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

1.4

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

1.41

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

1.59

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

1.42

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