A combined 3D-QSAR and docking studies for the In-silicoprediction of HIV-protease inhibitors

BMC Chemistry

Table 3 The statistics of all generated CoMFA models in order to obtained the best model

Charges	Model	GS	q²	SEP	C	r²	SEE	F	$r_{pred}^{2}$
Ligand-Based Method
Gasteiger Huckel	First	0.5	0.77	0.9	6	0.93	0.45	171.85	0.91
		1	0.77	0.9	6	0.93	0.47	173.31	0.91
		1.5	0.74	0.95	6	0.95	0.49	179.4	0.91
		2	0.73	0.92	5	0.92	0.5	181.12	0.91
	Best	2	0.71	1.03	6	0.94	0.44	212.63	0.96
AM1BCC	First	0.5	0.65	1.12	6	0.9	0.56	117.99	0.87
		1	0.64	1.13	6	0.9	0.56	118.9	0.88
		1.5	0.64	0.96	6	0.94	0.57	116.85	0.88
		2	0.74	1.01	6	0.93	0.42	217.07	0.9
	Best	2	0.72	0.94	6	0.92	0.49	173.85	0.9
MMFF94	First	0.5	0.74	0.94	5	0.92	0.51	175.71	0.92
		1	0.74	0.95	5	0.92	0.51	176.35	0.91
		1.5	0.72	0.98	5	0.92	0.51	172.22	0.91
		2	0.78	0.88	5	0.95	0.39	263.64	0.93
	Best	2	0.74	0.99	6	0.95	0.42	240.75	0.96
Structure-Based Method
MMFF94	Best	2	0.682	1.09	6	0.93	0.48	178.46	0.93

Where: GS grid spacing, q²: cross validated correlation coefficient, SEP Standard Error of Prediction, C optimal number of Components, r²: non-cross validated correlation coefficient, SEE Standard Error of Estimation, F Fischer test values, $r_{pred}^{2}$ : prediction of external test set for validation.

ISSN: 2661-801X