Table 2.

Summary of the modeling outcomes.

Toxicity value	Consensus model $Q^2$a	Model prediction absolute errorb	Mean value prediction absolute errorc	p-valued
RfD	0.41	0.77 (0.06, 1.80)	0.96 (0.08, 2.53)	$<0.0001$
RfD NOAEL	0.45	0.70 (0.06, 1.82)	0.93 (0.05, 2.37)	$<0.0001$
RfD BMD	0.31	0.88 (0.13, 2.08)	1.08 (0.09, 2.34)	0.0098
RfD BMDL	0.28	0.93 (0.07, 2.19)	1.13 (0.13, 2.44)	0.0098
OSF	0.33	0.92 (0.07, 2.45)	1.19 (0.12, 2.60)	$<0.0001$
CPV	0.25	0.97 (0.07, 2.53)	1.19 (0.15, 2.65)	0.0008
RfC	0.42	1.11 (0.12, 2.71)	1.49 (0.20, 3.54)	0.0015
IUR	0.42	0.93 (0.07, 2.69)	1.29 (0.06, 2.85)	$<0.0001$

Note: BMD, benchmark dose; BMDL, benchmark dose lower confidence limit; CPV, cancer potency value; IUR, inhalation unit risk; NOAEL, no observed adverse effect level; OSF, oral slope factor; QSAR, quantitative structure activity relationship; RfC = reference concentration; RfD = reference dose.

^a$Q^2$ is the fraction of the variance explained by each model, estimated by 5-fold cross-validation. In all cases, the accuracy of the model built with original data was significantly higher than that of models built using y-randomized data sets ($p<0.00001$).

^bThe mean and confidence interval (90%) of absolute error for the external prediction of each compound’s toxicity value against the QSAR model prediction under 5-fold cross-validation.

^cThe mean and confidence interval (90%) of absolute error for the external prediction of each compound’s toxicity value against the mean value of the compounds with that particular toxicity value.

^dKolmogorov-Smirnov statistics for the difference between model and “mean value” prediction absolute errors.