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: | 1757 | |
|---|---|---|
| Name: | ethyl 3-phenylprop-2-ynoate | |
| Description: | ||
| Labels: | Neutral | |
| CAS: | 2216-94-6 | |
| InChi Code: | InChI=1/C11H10O2/c1-2-13-11(12)9-8-10-6-4-3-5-7-10/h3-7H,2H2,1H3 |
Properties
pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)] i
| Value | Source or prediction |
|---|---|
| 1.67 |
experimental value |
| 1.37 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set) |
| 0.86 |
RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation) |
| 1.66 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set) |
| 1.39 |
SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation) |
| 1.08 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set) |
| 0.83 |
KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation) |
| 1.57 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set) |
| 1.37 |
XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation) |
| 1.58 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set) |
| 1.63 |
SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation) |
| 1.77 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set) |
| 1.54 |
DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation) |