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:1834
Name:2-phenylbenzene-1,4-diol
Description:
Labels:Neutral
CAS:1079-21-6
InChi Code:InChI=1/C12H10O2/c13-10-6-7-12(14)11(8-10)9-4-2-1-3-5-9/h1-8,13-14H

Properties

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

ValueSource or prediction
2.01

experimental value

1.7

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

1.07

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

1.32

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

0.98

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

1.41

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

1.11

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

1.78

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

1.19

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

1.47

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

1.05

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

1.84

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

1.09

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