The discovery of potential acetylcholinesterase inhibitors: A combination of pharmacophore modeling, virtual screening, and molecular docking studies

Table 1 The performance of 10 pharmacophoric hypotheses generated by HypoGen for AChE inhibitors

Hypotheses^a	Pharmacophoric features in generated hypotheses	RMS deviation	Cost Values				Residual cost^d
			Training set (R)^b	Error	Weight	Total^c
1	HBD, 4×HBic	1.411	0.851	270.24	1.170	289.972	142.57
2	HBD, 4×HBic	1.416	0.850	270.73	1.338	290.628	141.91
3	HBD, 4×HBic	1.419	0.849	270.97	1.144	290.681	141.86
4	HBD, 4×HBic	1.445	0.843	273.31	1.419	293.293	139.25
5	HBD, 3×HBic, RingArom	1.469	0.836	275.44	1.243	295.242	137.30
6	HBD, 4×HBic	1.474	0.834	275.93	1.145	295.636	136.91
7	HBD, 4×HBic	1.484	0.834	276.83	1.219	296.614	135.93
8	HBD, 4×HBic	1.510	0.827	279.20	1.163	298.925	133.62
9	HBD, 4×HBic	1.514	0.826	279.61	1.262	299.432	133.11
10	HBD, 4×HBic	1.520	0.825	280.21	1.127	299.899	132.64

^a Fischer randomization set at 95% confidence level was performed on all pharmacophore models.
^b Correlation coefficient (R) between the experimental activity and the estimated fit values of the training compounds.
^c Total costs = error cost + weight cost + configuration cost, where configuration cost = 18.564.
^d Residual cost = null cost - total cost, where null cost = 432.542.

ISSN: 1423-0127