Computational and in vitro antifungal discovery platform using modified flavonoids

Overview

This research program addresses the growing need for new antifungal agents by combining computational drug design with laboratory validation. The team focuses on modifying natural flavonoid compounds with diverse chemical substituents to enhance their binding affinity to fungal laccase receptors and improve antifungal activity. By integrating in silico screening with in vitro testing, the platform accelerates the identification of promising antifungal candidates derived from plant-based sources.

The approach is particularly relevant for pharmaceutical, agricultural, and biotechnology sectors seeking novel antifungal solutions. The combination of molecular dynamics simulations, ADME property assessment, and machine learning-based inhibition analysis offers a streamlined pathway from compound design to biological validation.

Technical specifications

Research workflow:

• Characterization of base flavonoids using T. versicolor laccase via spectroscopic methods
• Construction of a database of chemically modified flavonoid derivatives
• In silico ADME property evaluation to filter candidates with favorable pharmacokinetic profiles
• Molecular docking studies to calculate binding affinity against fungal receptors
• Molecular dynamics (MD) simulations to assess compound-receptor stability
• Chemical synthesis and structural confirmation of top candidates
• In vitro antifungal activity testing with minimum inhibitory concentration (MIC) measurement
• Image analysis using artificial intelligence to quantify inhibition zones
• Machine learning comparison of inhibition diameters across compounds

The platform leverages both computational chemistry tools and traditional microbiological assays, supported by AI-driven data analysis for objective measurement of antifungal effects.

Technology readiness level

The research group has prior experience investigating plant-derived extracts with antifungal activity in in vitro models. The current proposal extends this work by introducing computational molecule design validated through laboratory assays. Future validation steps include synthesis of designed molecules, in vitro antifungal testing, and AI-assisted inhibition analysis. The program is at an early-to-mid research stage, with the computational framework and in vitro protocols established but requiring full integration and experimental confirmation of the designed compounds.