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:	1745
	Name:	5-hydroxy-2-methyl-1,4-dihydronaphthalene-1,4-dione
	Description:
	Labels:	Neutral
	CAS:	481-42-5
	InChi Code:	InChI=1/C11H8O3/c1-6-5-9(13)10-7(11(6)14)3-2-4-8(10)12/h2-5,12H,1H3

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

Value	Source or prediction
2.2	experimental value
1.76	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (Training set)
1.11	RF: QSAR model for Tetrahymena pyriformis growth inhibition using the RF algorithm (10-fold cross-validation)
2.19	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)
1.51	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (Training set)
1.89	KNN: QSAR model for Tetrahymena pyriformis growth inhibition using the KNN algorithm (10-fold cross-validation)
2.12	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (Training set)
1.71	XGB: QSAR model for Tetrahymena pyriformis growth inhibition using the XGB algorithm (10-fold cross-validation)
2.18	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (Training set)
2.04	SNN: QSAR model for Tetrahymena pyriformis growth inhibition using the SNN algorithm (10-fold cross-validation)
1.88	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (Training set)
2.14	DNN: QSAR model for Tetrahymena pyriformis growth inhibition using the DNN algorithm (10-fold cross-validation)