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: | 403 | |
|---|---|---|
| Name: | 2-methylbutan-2-ol | |
| Description: | ||
| Labels: | Neutral | |
| CAS: | 75-85-4 | |
| InChi Code: | InChI=1/C5H12O/c1-4-5(2,3)6/h6H,4H2,1-3H3 |
Properties
pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)] i
| Value | Source or prediction |
|---|---|
| -1.22 |
experimental value |
| -1.21 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set) |
| -1.17 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation) |
| -1.59 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set) |
| -1.76 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation) |
| -1.1 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set) |
| -1.04 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation) |
| -1.22 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set) |
| -1.32 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation) |
| -1.33 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set) |
| -1.53 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation) |
| -1.28 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set) |
| -1.48 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation) |