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: | 1992 | |
|---|---|---|
| Name: | triphenyl phosphate | |
| Description: | ||
| Labels: | Neutral | |
| CAS: | 115-86-6 | |
| InChi Code: | InChI=1/C18H15O4P/c19-23(20-16-10-4-1-5-11-16,21-17-12-6-2-7-13-17)22-18-14-8-3-9-15-18/h1-15H |
Properties
pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)] i
| Value | Source or prediction |
|---|---|
| 1.81 |
experimental value |
| 1.58 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set) |
| 1.27 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation) |
| 1.8 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set) |
| 0.55 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation) |
| 0.72 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set) |
| 1.45 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation) |
| 1.79 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set) |
| 1.27 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation) |
| 1.16 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set) |
| 0.49 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation) |
| 1.57 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set) |
| 1.2 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation) |