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:753
Name:3-(prop-2-en-1-ylsulfanyl)prop-1-ene
Description:
Labels:Neutral
CAS:592-88-1
InChi Code:InChI=1/C6H10S/c1-3-5-7-6-4-2/h3-4H,1-2,5-6H2

Properties

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

ValueSource or prediction
-0.31

experimental value

-0.08

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

0.25

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

-0.3

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

0.38

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

-0.39

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

0.58

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

-0.18

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

0.31

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

-0.14

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

0.16

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

-0.32

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

0.32

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