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:	1193
	Name:	3-chloro-2-fluoro-5-(trifluoromethyl)benzaldehyde
	Description:
	Labels:	Neutral
	CAS:	261763-02-4
	InChi Code:	InChI=1/C8H3ClF4O/c9-6-2-5(8(11,12)13)1-4(3-14)7(6)10/h1-3H

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

Value	Source or prediction
1.48	experimental value
1.44	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set)
1.26	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation)
1.47	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set)
0.58	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation)
1.06	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set)
1.11	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation)
1.47	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set)
1.28	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation)
1.29	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set)
0.73	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation)
1.34	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set)
0.61	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation)