Integrated 2D- and 3D-QSAR Modeling Reveals Structural Determinants Governing the Bioactivity of Triazine Derivatives Against COPD

Background The class of heterocyclic compounds known as triazine derivatives is significant and has the potential to treat chronic obstructive pulmonary disease (COPD). The identification of structural determinants and the prediction of bioactivity are made possible by Quantitative Structure–Activity Relationship (QSAR) approaches. This study evaluated the steric and hydrophobic contributions governing the activity of triazine derivatives using integrated 2D- and 3D-QSAR methodologies. Methods V-Life MDS was used to model a dataset of 34 triazine derivatives following energy minimisation using the Merck Molecular Force Field (MMFF). Partial Least Squares (PLS) regression with stepwise selection was used to create 2D-QSAR models, and k-Nearest Neighbour Molecular Field Analysis (kNN-MFA) was used to develop 3D-QSAR models. Z-score randomisation, F-test, r², q², and pred_r² were all used in the statistical validation. Results Strong predictivity was shown by the ideal 2D-QSAR Model (Model I) (r² = 0.7876, q² = 0.7146, pred_r² = 0.622). Descriptor analysis showed that while aliphatic CH₂ groups harmed potency, hydrophobic surface area and aromatic connectivity (Saa CHE-index) increased activity. With q2 = 0.7388 and pred_r2 = 0.4073, the 3D-QSAR model (k = 2) demonstrated good internal stability but moderate external predictivity. Important information about substitution patterns was provided by steric contour maps, which showed that bioactivity was favoured by decreased steric hindrance at lattice points S_952 and S_668. Conclusion The combined 2D- and 3D-QSAR results show that steric modulation, aromatic connectivity, and hydrophobic interactions are important activity determinants. The logical development of optimised triazine derivatives with improved therapeutic potential against COPD is supported by these findings.