In silico discovery and prioritization of novel antifungal compounds for rice blast disease

Overview

The proposed solution provides a computational approach to identify novel antifungal compounds targeting Pyricularia oryzae, the causative agent of rice blast disease. By leveraging virtual screening and predictive modeling, the approach aims to prioritize compounds with high biological activity, selectivity for rice, and field-compatible properties such as stability and solubility. This method is intended to reduce the time and costs associated with traditional synthesis and testing, supporting sustainable and innovative agricultural solutions.

Technical specifications

• Target and compound identification: Bioinformatic analysis and 3D modeling of P. oryzae protein targets to preselect antifungal-like compounds.
• Pharmacophore and 3D-QSAR modeling: Construction of a pharmacophore hypothesis and a 3D-QSAR model to guide compound screening.
• Virtual screening: Utilization of molecular docking to predict protein–ligand interactions and estimate biological activity.
• Advanced computational validation: Application of ADMET filters and physicochemical profiling, followed by molecular dynamics and MM-GBSA analysis to assess binding stability.

Technology readiness level

Currently at TRL 3, the technology has been validated through in silico studies, with plans for biological validation to further develop and test prioritized candidates in laboratory or greenhouse settings.