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 3 External validation parameters of the 2D-QSAR models

External validation metrics	GFA-MLR model	GFA-ANN (4-5-1) model	Threshold	Comment	References
Pearson correlation squared (\(r_{{{\text{test}}}}^{2}\))	0.8943	0.8831	R²_test > 0.6	Passed	[37]
\(R_{{{\text{pred}}}}^{2}\)	0.7745	0.7763	\(R_{pred}^{2}\) > 0.5	Passed	[37]
Δ\(\overline{R}_{m}^{2}\)(test)	0.0804	–	< 0.5	Passed
\(r_{0}^{2}\)	0.8943	–	> 0.5	Passed
RMSEP	0.1835	0.1813	–	–
\(r_{0}^{\prime 2}\)	0.8923	–	> 0.5	Passed	[37]
\(\left\| {r_{0}^{2} - r_{0}^{\prime } } \right\|\)	0.0172	–	\(\left\| {r_{0}^{2} - r_{0}^{\prime } } \right\|\)< 0.3	Passed