Table 1. QSAR Model Validations on the External 5-Fold CV Sets As Well As the Additional Independent External Set from WOMBAT.

			confusion matrix							statistics
machine learning methods	external sets	prediction CCR	N(1)a	N(2)a	TP	TN	FP	FN		SE	SP	EN(1)	EN(2)
	1	0.86	19b	14	17	11	3	2		0.89	0.79	1.61	1.76
	2	0.61	20	13	15	6	7	5		0.75	0.46	1.16	1.30
k-nearest neighbor	3	0.77	22	11	20	7	4	2		0.91	0.64	1.43	1.75
	4	0.86	20	13	19	10	3	1		0.95	0.77	1.61	1.88
	5	0.68	23	10	22	4	6	1		0.96	0.40	1.23	1.80
	Cumulative	0.76	104	61	93	38	23	11		0.89	0.62	1.41	1.71
	WOMBAT	N/A	66	0	62	N/A	N/A	4		0.94	N/A	N/A	N/A
	1	0.80	20	14	16	11	3	4		0.80	0.79	1.58	1.59
	2	0.68	20	13	15	8	5	5		0.75	0.62	1.32	1.42
random forest	3	0.84	22	11	21	8	3	1		0.95	0.73	1.56	1.88
	4	0.74	20	13	19	7	6	1		0.95	0.54	1.35	1.83
	5	0.83	23	10	22	7	3	1		0.96	0.70	1.52	1.88
	Cumulative	0.78	105	61	93	41	20	12		0.89	0.67	1.46	1.71
	WOMBAT	N/A	66	0	62	N/A	N/A	4		0.94	N/A	N/A	N/A
	1	0.87	20	14	19	11	3	1		0.95	0.79	1.36	1.88
	2	0.68	20	13	18	6	7	2		0.90	0.46	1.25	1.64
support vector machines	3	0.95	22	11	22	10	1	0		1.00	0.91	1.83	2.00
	4	0.76	20	13	18	8	5	2		0.90	0.62	1.40	1.72
	5	0.76	23	10	21	6	4	2		0.91	0.60	1.39	1.75
	Cumulative	0.80	105	61	98	41	20	7		0.93	0.67	1.48	1.82
	WOMBAT	N/A	66	0	62	N/A	N/A	4		0.96	N/A	N/A	N/A

^aN(1) = number of actives, N(2) = number of inactives, TP = true positive (actives predicted as actives), FP = false positives (inactives predicted as actives), FN = false negatives (actives predicted as inactives), TN = true negative (inactives predicted as inactives), SE = sensitivity = TP/N(1), SP = specificity = TN/N(2), EN = the normalized enrichment, EN(1) = (2TP × N(2))/(TP × N(2) + FP × N(1)), EN(2) = (2TN × N(1))/(TN × N(1) + FN × N(2)), and CCR = correct classification rate.

^bSome N(1) actives of and N(2) inactives were out of application domain of all consensus models, thus having no prediction. Only data for compounds found within the AD were used for statistical summaries.