Skip to main content

Table 2 Internal validation of the 2D-QSAR models

From: 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

Internal validation metrics GFA-MLR model GFA-ANN (4-5-1) model Threshold Comment References
Lack of fit (LOF) 0.0546    
Pearson correlation (r) 0.9590 0.9610 R > 0.6 Passed [37]
Pearson correlation squared (\(R_{{\text{train }}}^{2}\)) 0.9192 0.9227 R2train > 0.6 Passed [37]
Adjusted R2 (R2adj) 0.8991 R2adj > 0.6 Passed [37]
Spearman rank correlation (ρ) 0.9155 0.9220 ρ > 0.6 Passed  
Root-mean-square error (RMSE) 0.0959 0.0940 Low Passed  
Cross-validated squared (Q2) 0.8767 0.9212 Q2 > 0.6 Passed [37]
Y-randomization (\(cR_{p}^{2} )\) 0.8300 \(cR_{p}^{2}\) > 0.6 Passed [38]