Molecular Docking and QSAR studies for Modeling Antifungal Activity of Triazine Analogues against Therapeutic Target NMT of Candida albicans

Abstract

The s-triazine derivatives have been attracting the attention of researchers due to a broad range of biological applications. Present research deals with a combination of genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure–activity relationships (QSAR) modeling and molecular docking as relevant to triazine analogs in an attempt to investigate their role as novel NMT inhibitors of Candida albicans. A penta-varient model which assure all validation criteria up to considerable echelon (R2 = 0.792, Q2 = 0.679 and = 0.603) supplemented by multicollinearity diagnosis by VIF and tolerance data analysis, signaling the robustness of the QSAR model. The descriptors RDF040v, Ds, Mor04m, X4v, and MATS2p in the projected QSAR model have quantified the role of atomic properties such as topology; atomic van der Waals volume, mass, and polarizability execute vital part to modify the antifungal activity of compounds under investigation. Further, a molecular docking simulation study revealed that three compounds, in particular, showed a superior binding affinity with a re-rank score of -142.594, -138.972, -137.540 kcal/mol. Consequently, this study may turn out to be helpful towards the development and optimization of existing antifungal activity of compounds under investigation.