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 7 Computed model descriptor values

Name	ATS7s	SpMax5_Bhv	nHBint6	TDB9m	Activity	MLR Train (4D)	ANN Train (4-5-1)
Training set
8	328.6200	3.1143	2.0000	355.3020	0.6059	0.6071	0.6258
9	343.3860	3.1514	2.0000	409.0160	0.4629	0.5062	0.5131
10	337.3610	2.8954	2.0000	265.4610	0.2304	0.2049	0.1982
12	317.3060	2.9889	2.0000	216.9720	0.6740	0.6943	0.7293
13	421.9410	3.1621	2.0000	356.1220	0.5128	0.4547	0.4694
14	459.1530	3.0036	2.0000	311.7580	-0.0400	0.0035	-0.0010
15	442.4260	3.0034	2.0000	358.3800	-0.1727	-0.0877	-0.0737
16	296.8580	2.9538	3.0000	149.4700	0.2809	0.2918	0.2686
17	295.9440	2.9534	2.0000	142.5230	0.9645	0.8840	0.8928
19	280.5280	2.8973	3.0000	62.4053	0.5059	0.4420	0.4644
20	203.8400	2.9016	2.0000	175.6480	0.7844	0.9163	0.9181
21	303.7690	2.9523	2.0000	226.0140	0.6868	0.6000	0.6363
25	225.2410	2.9486	2.0000	237.9970	0.8119	0.7980	0.8246
27	270.9540	2.9540	3.0000	114.5590	0.4663	0.4806	0.4868
28	360.7500	2.9938	2.0000	159.4810	0.6686	0.7493	0.7844
29	344.3980	2.9933	2.0000	233.2870	0.8661	0.5725	0.6026
3	394.8890	3.1292	3.0000	213.5830	0.2278	0.3097	0.2492
4	375.9440	2.9013	3.0000	206.1430	-0.3290	-0.2851	-0.1862
23	230.4260	2.8838	3.0000	109.1630	0.5023	0.4152	0.4411
24	237.1110	2.8809	2.0000	114.7140	0.7578	0.9378	0.9351
5	304.0900	2.9121	2.0000	333.6530	0.1772	0.1495	0.1355
Test set
7	353.7690	3.0239	2.0000	481.0830	-0.1833	-0.1260	-0.1076
22	228.3700	2.9538	2.0000	313.1860	0.5697	0.5729	0.6014
26	317.0560	2.9527	2.0000	102.8700	1.0589	0.9375	0.9287
2	293.9720	3.0101	2.0000	300.9610	0.7134	0.5724	0.5947
6	400.0460	2.9753	2.0000	309.6650	0.3752	0.1107	0.0919
1	392.1160	3.0356	3.0000	190.2440	0.1772	0.1119	0.0615
11	325.4910	3.0576	2.0000	314.3700	0.9423	0.5740	0.5936
18	298.7220	2.9542	3.0000	88.4821	0.6219	0.4743	0.4797