Exploratory 3D-QSAR and Molecular Docking Analysis of 1, 3-thiazinesubstituted 1, 3, 5-triazine Derivatives as Potential Antimalarial Agents
Manish Pathak, Lubhan Singh, Sokindra Kumar, Akhilesh Kumar Mishra, Vivek Pal, Akansha DwivediIntroduction:
Triazine scaffolds offer appealing models for developing enzymefocused therapies that can overcome resistance due to their structural flexibility and target selectivity. In this work, a short, chemically homogeneous series of 1,3-thiazine-substituted 1,3,5-triazine derivatives was subjected to an initial exploratory structure-activity relationship investigation and to molecular docking to investigate their possible dual-target binding interactions.
Methods:
A three-descriptor Multiple Linear Regression model was developed to correlate N, EHOMO, and SB parameters with experimental antimalarial activity (pIC50). Y-randomization and leave-one-out cross-validation were used to evaluate the model's robustness. Molecular docking was done against P. falciparum plasmepsin-II and DNA gyrase.
Results:
The MLR model demonstrated high structural homogeneity (R2 = 0.964; adjusted R2 = 0.948). Electronic descriptors, molecular polarisability, and topological variables all had a significant influence on predicted activity. Based on pIC₂⁽ values, compound 6j showed the highest predicted antimalarial efficacy. Compound 6e, on the other hand, showed the most advantageous and reliable dual-target binding interactions with DNA gyrase and Plasmepsin-II, suggesting it could be a good lead candidate for further optimization.
Discussion:
The results imply that the triazine scaffold's electronic and steric characteristics are essential for regulating antimalarial activity. A potentially complementary therapeutic approach is indicated by the identified enzyme-targeted binding profile, which differs mechanistically from traditional medications such as artemisinin and chloroquine.
Conclusion:
Compound 6j was the most active lead candidate for predicted antimalarial activity. To verify biological efficacy, experimental validation is necessary, as the study is computational and relies on a small dataset.