Key Takeaways
- AgPlenus, a subsidiary of Evogene Ltd. (NASDAQ/TASE: EVGN), has launched its Antifungal Potency Predictor (APP), a machine learning model that predicts antifungal potency directly from chemical structure.
- The model expands Evogene's ChemPass AI for Ag™ platform by forecasting biological efficacy before chemical synthesis and fungal assay validation.
- The global fungicide market is estimated at approximately $22 billion annually, as fungal diseases and growing resistance to existing products threaten crop yields and food security.
- AgPlenus expects the APP model to support its internal fungicide pipeline, including its APTF-1 target against Septoria wheat blotch, and future work on pathogens such as Botrytis and Fusarium.
- AgPlenus and Evogene plan to co-develop additional predictive AI models to forecast other biological attributes in the crop protection discovery process.
AgPlenus Launches Antifungal Potency Predictor
AgPlenus, a company developing crop protection products and a subsidiary of Evogene Ltd. (NASDAQ: EVGN) (TASE: EVGN), has announced the launch of its Antifungal Potency Predictor (APP). The new machine learning model predicts the antifungal potency of small molecules directly from their chemical structures, expanding the capabilities of Evogene's ChemPass AI for Ag™ platform by forecasting biological efficacy prior to chemical synthesis and fungal assay validation.
Addressing Fungicide Resistance
According to AgPlenus, the global fungicide market is worth approximately $22 billion annually, with fungal diseases causing tens of billions of dollars in crop losses each year and posing a growing threat to food security. The company said widespread and repetitive use of existing fungicides has accelerated the emergence of resistant fungal pathogens, reducing the long-term effectiveness of many commercial products and creating demand for novel fungicides with new modes of action.
How the New AI Model Works
The APP model was developed using machine learning algorithms trained on AgPlenus' proprietary curated datasets. It builds on the ChemPass AI for Ag™ platform's existing ability to identify crop protection targets and generate molecules with high target-protein affinity, extending those capabilities to predict how a small molecule will perform within the fungus itself. By forecasting antifungal potency at the earliest discovery stages, AgPlenus said the model reduces the number of molecules that require experimental evaluation, allowing resources to focus on candidates with the highest probability of success.
“In 2025, we demonstrated the power of the ChemPass AI for Ag™ platform to identify novel target proteins capable of overcoming resistance, as well as novel active small molecules combating devastating crop diseases like Septoria wheat blotch. Today, we are taking another major step forward with the launch of our Antifungal Potency Predictor. By enabling us to forecast antifungal potency directly from molecular structure, prior to chemical synthesis, the APP model allows us to identify and prioritize high-quality candidates at the earliest stages of discovery. I am excited to see this breakthrough model integrated into ChemPass AI for Ag™, further strengthening our ability to advance current and future product development programs,” said Dr. Dan J. Gelvan, CEO of AgPlenus.
Impact on AgPlenus' Fungicide Pipeline
AgPlenus expects the APP model to support its internal fungicide pipeline, including its APTF-1 target designed to combat Septoria wheat blotch, as well as planned pipeline expansions targeting other pathogens such as Botrytis and Fusarium. The company said the model also lays the groundwork for additional predictive AI models it plans to co-develop with Evogene to forecast other biological attributes throughout the crop protection discovery process.
