Table 3.

Statistical data of MLR models generated from the pharmacophoric characteristics of the training set in respect to pICT₅₀ values.

Mono-parametric Models
Eq.	Descriptor	R	R2	R2A	SEE	F
1	GF	0.8004	0.6406	0.6217	0.2703	33.8612
2	A	0.7675	0.5890	0.5674	0.2890	27.2268
3	SF	0.7383	0.5451	0.5212	0.3040	22.7680
4	Ar	0.0876	0.0076	-0.0445	0.4491	0.1470
Di-parametric Models
Eq.	Descriptor	R	R2	R2A	SEE	F
1	GF +Ar	0.8177	0.6686	0.6317	0.2666	18.1548
2	GF + SF	0.8092	0.6548	0.6164	0.2721	17.0700
3	A + GF	0.8068	0.6510	0.6122	0.2737	16.7865
4	A + Ar	0.7990	0.6384	0.5982	0.2786	15.8859
5	A + SF	0.7698	0.5926	0.5473	0.2956	13.0909
6	SF + Ar	0.7559	0.5714	0.5238	0.3032	11.9987
Tri-parametric Models
Eq.	Descriptor	R	R2	R2A	SEE	F
1	A + GF + SF	0.8336	0.6949	0.6410	0.2632	12.9045
2	A + GF + Ar	0.8312	0.6909	0.6363	0.2650	12.6643
3	GF + SF + Ar	0.8238	0.6787	0.6220	0.2701	11.9706
4	A + SF + Ar	0.7999	0.6399	0.5764	0.2859	10.0714
Tetra-parametric Model
Eq.	Descriptor	R	R2	R2A	SEE	F
1	A + SF + GF + Ar	0.8587	0.7374	0.6717	0.2517	11.2318