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:	1638
	Name:	5-isothiocyanato-2,3-dihydro-1H-indene
	Description:
	Labels:	Neutral
	CAS:	149865-84-9
	InChi Code:	InChI=1/C10H9NS/c12-7-11-10-5-4-8-2-1-3-9(8)6-10/h4-6H,1-3H2

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

Value	Source or prediction
2.48	experimental value
2.28	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set)
1.94	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation)
2.47	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (Training set)
2.45	SVM: QSAR model for Tetrahymena pyriformis growth inhibition using the SVM algorithm (10-fold cross-validation)
2.44	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set)
2.66	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation)
2.53	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set)
2.11	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation)
2.49	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set)
3.16	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation)
2.48	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set)
3.28	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation)