Computational Discovery of New Fungicide Candidates to Protect Potato Crops
Faculty Mentor Information
Dr. Jagdish Patel, University of Idaho
Presentation Date
7-15-2026
Abstract
Potato crops are vulnerable to fungal diseases that cause billions of dollars in agricultural losses each year and threaten global food security. The increasing resistance of fungal pathogens to existing fungicides highlights the urgent need for new treatments with novel mechanisms of action. One promising target is YEF3, a protein essential for fungal protein synthesis that is unique to fungi and absent in plants and humans, making it an attractive target for developing selective fungicides.
This project used computational drug discovery to identify improved small molecules that may inhibit YEF3. Previously identified lead compounds served as starting points to generate a large library of chemically related molecules through similarity and substructure searches. More than 770,000 candidate compounds were collected and filtered using physicochemical properties associated with known active molecules, resulting in approximately 330,000 compounds for virtual screening. These compounds were screened against three potential YEF3 binding sites using molecular docking and high-performance computing to predict their binding affinity.
The virtual screening identified several compounds with predicted binding affinities that exceeded those of the original lead molecules, providing a prioritized set of candidates for future experimental testing. These findings demonstrate how computational screening can rapidly narrow hundreds of thousands of molecules to a manageable number of promising compounds, accelerating the early stages of fungicide discovery. The candidate molecules generated in this study provide a strong foundation for experimental validation and the development of next-generation fungicides to protect potato crops and promote sustainable agriculture.
Computational Discovery of New Fungicide Candidates to Protect Potato Crops
Potato crops are vulnerable to fungal diseases that cause billions of dollars in agricultural losses each year and threaten global food security. The increasing resistance of fungal pathogens to existing fungicides highlights the urgent need for new treatments with novel mechanisms of action. One promising target is YEF3, a protein essential for fungal protein synthesis that is unique to fungi and absent in plants and humans, making it an attractive target for developing selective fungicides.
This project used computational drug discovery to identify improved small molecules that may inhibit YEF3. Previously identified lead compounds served as starting points to generate a large library of chemically related molecules through similarity and substructure searches. More than 770,000 candidate compounds were collected and filtered using physicochemical properties associated with known active molecules, resulting in approximately 330,000 compounds for virtual screening. These compounds were screened against three potential YEF3 binding sites using molecular docking and high-performance computing to predict their binding affinity.
The virtual screening identified several compounds with predicted binding affinities that exceeded those of the original lead molecules, providing a prioritized set of candidates for future experimental testing. These findings demonstrate how computational screening can rapidly narrow hundreds of thousands of molecules to a manageable number of promising compounds, accelerating the early stages of fungicide discovery. The candidate molecules generated in this study provide a strong foundation for experimental validation and the development of next-generation fungicides to protect potato crops and promote sustainable agriculture.