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: | 1396 | |
|---|---|---|
| Name: | 3-methylbutyl prop-2-enoate | |
| Description: | ||
| Labels: | Neutral | |
| CAS: | 4245-35-6 | |
| InChi Code: | InChI=1/C8H14O2/c1-4-8(9)10-6-5-7(2)3/h4,7H,1,5-6H2,2-3H3 |
Properties
pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)] i
| Value | Source or prediction |
|---|---|
| 0.41 |
experimental value |
| 0.31 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set) |
| 0.09 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation) |
| 0.4 |
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) |
| 0.22 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set) |
| -0.03 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation) |
| 0.33 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set) |
| -0.01 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation) |
| 0.46 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set) |
| 0.41 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation) |
| 0.4 |
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) |