Abstract

Introduction: Fungal infection and resistance to existing antibiotics is a major problem across the globe, which is associated with the inappropriate use of antibiotics in humans, animals farming, and agriculture. Unfortunately, addressing these difficulties is becoming extremely critical as they arise. In this paper, we used Hansch and Fujita's linear free energy relationship (LFER) model to perform 2D QSAR (quantitative structure activity relationships) on 28 known Indole derivatives.

Objective: Molecular docking and 2D QSAR studies of Indole derivatives as antifungal agents.

Methods: 2D QSAR was performed on the reported antifungal activity data of 28 selected compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of Indole derivatives to antifungal activity. To represent the structural properties of compounds, a collection of molecular descriptors such as geometric, topological, hydrophobic and electronic characters were determined. Biological activity data was first translated into pMIC values (i.e. -log MIC) and used as a predictor variable in the QSAR analysis. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger.

Results: All docked compounds had better binding scores with reference to fluconazole.

Conclusion: Compounds with high values of electronic energy and dipole moment will be effective against fungi, and Indole derivatives having lowered κ2 values will be effective antifungal agents.