Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Beni-Suef University Journal of Basic and Applied Sciences

Table 10 Statistical validation results of all possible CoMSIA models

S. No.	Descriptors	Q²	R²	SEE	N
1	Steric (S)	0.639	0.963	0.0771	5
2	Electrostatic (E)	0.662	0.901	0.1216	4
3	Hydrophobic (H)	0.651	0.967	0.0726	5
4	H-Bond Donor(D)	0.659	0.900	0.1223	4
5	H-Bond Acceptor(A)	0.600	0.943	0.0956	5
6	S + E	0.655	0.901	0.1216	4
7	S + H	0.646	0.968	0.0720	5
8	S + D	0.662	0.904	0.1199	4
9	S + A	0.597	0.943	0.0952	5
10	E + H	0.648	0.946	0.0931	5
11	E + D	0.701	0.873	0.1338	3
12	E + A	0.619	0.944	0.0944	5
13	H + D	0.660	0.906	0.1185	4
14	H + A	0.602	0.950	0.0895	5
15	S + E + H	0.647	0.973	0.0682	6
16	S + E + D	0.702	0.877	0.1318	3
17	S + E + A	0.627	0.943	0.0952	5
18	S + H + A	0.600	0.950	0.0894	5
19	S + H + D	0.661	0.909	0.1167	4
20	S + A + D	0.610	0.900	0.1222	4
21	E + A + D	0.662	0.865	0.1378	3
22	E + A + H	0.629	0.951	0.0886	5
23	A + H + D	0.608	0.901	0.1217	4
24	E + D + H	0.699	0.879	0.1306	3
25	S + E + H + D	0.700	0.882	0.1288	3
26	S + E + H + A	0.636	0.950	0.0890	5
27	S + H + D + A	0.661	0.871	0.1350	3
28	E + H + D + A	0.661	0.871	0.1353	3
29	S + E + D + A	0.665	0.870	0.1353	3
30	S + E + H + D + A	0.664	0.875	0.1327	3