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:	1122
	Name:	ethyl (3R)-4-cyano-3-hydroxybutanoate
	Description:
	Labels:	Neutral
	CAS:	141942-85-0
	InChi Code:	InChI=1/C7H11NO3/c1-2-11-7(10)5-6(9)3-4-8/h6,9H,2-3,5H2,1H3/t6-/m1/s1

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

Value	Source or prediction
-1.64	experimental value
-1.2	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set)
-0.52	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation)
-1.64	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set)
-1.66	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation)
-1.57	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set)
-0.82	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation)
-1.51	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set)
-0.53	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation)
-1.53	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set)
-1.6	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)
-1.3	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation)