AgPlenus Launches AI Model for Predicting Antifungal Potency

AgPlenus, a subsidiary of Evogene Ltd., announced in a press release the launch of its Antifungal Potency Predictor (APP), a machine learning model designed to estimate the antifungal potency of small molecules based on their chemical structures. The tool extends the ChemPass AI for Ag platform by forecasting biological efficacy before chemical synthesis and laboratory validation.

The APP model uses machine learning algorithms trained on curated datasets to predict antifungal activity in early research stages. This capability allows the prioritization of molecules more likely to succeed in biological testing, reducing the number of compounds that require experimental evaluation.

The model is expected to strengthen AgPlenus' internal fungicide pipeline, including the APTF-1 target aimed at combating diseases such as Septoria wheat blotch. It will also assist in developing treatments for additional pathogens, including Botrytis and Fusarium. AgPlenus and Evogene plan to continue developing predictive AI models for other biological attributes in crop protection research.