Abstract
This paper explores the confluence of genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure–activity relationship (QSAR) modeling and molecular docking simulation studies relevant to the novel and effective triazole derivatives as NMT inhibitors in an attempt to develop superior antifungal activity coupled with less susceptibility to develop resistance. Initially, thirteen penta-variant models were generated through hybrid GA-MLR based QSAR approach. Eventually, a general pooled model was derived through the collective consideration of all the descriptors incorporated in the stated thirteen models. The model was found to be predictive model as per traditional internal and external validation parameters (R2 = 0.829, Q2 = 0.759, $${R}_{\mathrm{adj}}^{2}\hspace{0.17em}$$ = 0.796, $${R}_{\mathrm{Pred}}^{2}$$ = 0.613) along with Y-randomization test and recently proposed various $$r_{m}^{2}$$ metrics. The proposed model is in agreement with Golbraikh and Tropsha recommendations supplemented by non-existence of multicollinearity via ridge trace, VIF trace and tolerance data analysis. The modeled descriptors signify the role of atomic van der Waals volume, nitrogen-containing fragment, number of hydrogen atoms and hydrogen bonding in modulating antifungal activity. Further, the molecular docking simulation studies reinforce the role of hydrogen bonding interactions in modulating the activity; as reflected by QSAR analysis; docking poses have revealed that four triazole derivatives were profusely bonded through hydrophobic, electrostatic and hydrogen bond interactions and stabilize into the active site of target protein NMT. Asn392 and His227 are key amino acid residues liable for generation of hydrogen bond interactions whereas Leu350 and Tyr354 amino acid residues were accountable for steric interactions. Based on the studies the two molecules are hypothesized and proposed with optimized biological function.
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