Table 3. Summary of Machine-Learning Models Based on BestFirst Feature Selection Method with the Internal Test Set^a.

		confusion matrix
descriptors	models	TP	TN	FP	FN	sensitivity	specificity	accuracy	G-mean
MOEb	RF	215	112	60	20	0.91	0.65	0.80	0.77
	SVM	219	109	63	16	0.93	0.63	0.81	0.77
	KNN	215	114	58	20	0.91	0.66	0.81	0.78
	BQSAR	196	120	52	39	0.83	0.70	0.78	0.76
MACCSc	RF	207	96	76	28	0.88	0.56	0.74	0.70
	SVM	199	75	97	36	0.85	0.44	0.67	0.61
	KNN	215	79	93	20	0.91	0.46	0.72	0.65
	BQSAR	158	117	55	77	0.67	0.68	0.68	0.68
SS-FPd	RF	215	73	99	20	0.91	0.42	0.71	0.62
	SVM	220	66	106	15	0.94	0.38	0.70	0.60
	KNN	220	67	105	15	0.94	0.39	0.71	0.60
	BQSAR	188	86	86	47	0.80	0.50	0.67	0.63
combinede	RF	215	118	54	20	0.91	0.69	0.82	0.79
	SVM	219	106	66	16	0.93	0.62	0.80	0.76
	KNN	207	124	48	28	0.88	0.72	0.81	0.80
	BQSAR	193	118	54	42	0.82	0.69	0.76	0.75

^aNote: RF, random forest; SVM, support vector machine, KNN, kappa nearest neighbor; BQSAR, binary QSAR.

^bBestFirst descriptors from 2D-MOE.

^cBestFirst descriptors from MACCS fingerprints.

^dSubstructure fingerprints.

^eBestFirst descriptors from all the calculated descriptors.