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

pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)] i

Value	Source or prediction
3.52	experimental value
3.03	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set)
2.25	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation)
3.51	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set)
2.4	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation)
2.52	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set)
2.14	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation)
3.37	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set)
2.24	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation)
3.42	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set)
3.09	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation)
3.5	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set)
3.22	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation)