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:	1117
	Name:	N-(prop-2-enamidomethyl)prop-2-enamide
	Description:
	Labels:	Neutral
	CAS:	110-26-9
	InChi Code:	InChI=1/C7H10N2O2/c1-3-6(10)8-5-9-7(11)4-2/h3-4H,1-2,5H2,(H,8,10)(H,9,11)/f/h8-9H

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

Value	Source or prediction
-0.51	experimental value
-0.41	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set)
-0.23	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation)
-0.52	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set)
-0.45	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation)
-0.38	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set)
0.15	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation)
-0.51	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set)
-0.19	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation)
-0.43	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set)
-0.37	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation)
-0.47	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set)
-0.23	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation)