Table 2.

Summary of 3D model performance

Model	ANNE	SVM	MLR	KPLS	RF	PLS
Training set
MAE	0.19	0.21	0.20	0.22	0.10	0.22
Kendall	0.64	0.62	0.62	0.58	0.86	0.60
SCI	0.67	0.73	0.87	0.86	0.99	0.13
S(0)	0.64	0.62	0.62	0.58	0.86	0.59
S(1)	1.00	1.00	1.00	1.00	1.00	-1.00
Test Set
MAE	0.20	0.22	0.23	0.25	0.20	0.23
Kendall	0.55	0.52	0.49	0.43	0.56	0.48
SCI	0.93	0.83	0.83	-0.74	-0.66	0.73
S(0)	0.56	0.52	0.50	0.44	0.57	0.49
S(1)	1.00	1.00	-1.00	-1.00	-1.00	1.00
Prospective Set
MAE	0.35	0.34	0.74	0.34	*	0.32
Kendall	0.34	0.35	-0.09	0.38	*	0.34
SCI	-0.65	-0.49	-0.90	0.80	*	-0.69
S(0)	0.35	0.36	-0.07	0.39	*	0.35
S(1)	-1.00	-1.00	-1.00	1.00	*	-1.00

Models using ADMET 3D Predictor descriptors and Kohonen map: ANNE, ADMET Predictor neural net; SVM, ADMET Predictor support vector machine; MLR, ADMET Predictor multiple linear regression; KPLS, ADMET Predictor kernel partial least squares; RF, Pipeline Pilot random forest; PLS, SIMCA-P+ partial least squares. The performance properties of the models were calculated as described in CALCULATIONS AND STATISTICS. The properties were not calculated for RF since prediction outliers could not be identified