Molecular docking and QSAR theoretical model for prediction of phthalazinone derivatives as new class of potent dengue virus inhibitors

Abstract

Background

Dengue fever is a key public health unease in various tropical and sub-tropical regions. The improvement of existing agents that can inhibit the dengue virus is therefore of utmost importance. In this work, the QSAR study was carried out on 25 molecules of phthalazinone derivatives which have been reported to possess excellent dengue virus inhibitory activity. Density functional computational technique was used in the optimisation of the molecules with the basis set at theory level (B₃LYP, 6-31G*) respectively. The multiple linear regression (MLR) model was built using genetic function approximation (GFA) in the material studio software package. Also, in this study, molecular docking simulation was carried between dengue virus serotype 2 protease (PDB CODE: 6mol) and some selected phthalazinone derivatives (compounds 1, 2, 7, 11, and 21).

Results

The model was robust as evidenced by validation and robustness statistical parameter which include predicted R²_pred., adjusted R²_adj., cross-validated Q² and R² regression coefficient, etc (R²_pred. = 0.71922, R²_adj. = 0.939699, Q²_CV = 0.905909, R² = 0.955567) respectively. The molecular docking studies conducted in this study have outlined the binding affinities of the selected compounds (1, 2, 7 11, and 21) which are all in good correlation with their respective pIC₅₀ values. The free binding affinities of the selected compounds were found to be (− 8.7, − 8.8, − 8.7, − 8.3, and − 8.9 kcal/mol) respectively, compound 21 with the binding affinity of − 8.9 kcal/mol had the best binding free energy with the protease relative to other compounds under consideration.

Conclusion

The MLR-GFA model study alongside with the molecular docking analysis has essentially provided a valuable and in-depth understanding as well as knowledge for the development of novel chemical compounds with enhanced inhibitory potential against the dengue virus serotype 2 (DNV-2). Hence, the developed model can be applicable in predicting the anti-dengue activity of a new set of chemical compounds that fall within its applicability domain.