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: | 936 | |
|---|---|---|
| Name: | 1-chloro-5-methyl-2,4-dinitrobenzene | |
| Description: | ||
| Labels: | Neutral | |
| CAS: | 51676-74-5 | |
| InChi Code: | InChI=1/C7H5ClN2O4/c1-4-2-5(8)7(10(13)14)3-6(4)9(11)12/h2-3H,1H3 |
Properties
pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)] i
| Value | Source or prediction |
|---|---|
| 2.33 |
experimental value |
| 2.03 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set) |
| 1.46 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation) |
| 2.32 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set) |
| 1.47 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation) |
| 1.72 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set) |
| 1.33 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation) |
| 2.26 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set) |
| 1.76 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation) |
| 2.03 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set) |
| 1.43 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation) |
| 2.23 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set) |
| 1.56 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation) |