AgPlenus Launches AI Model to Predict Antifungal Potency
AgPlenus has launched a new AI model, the Antifungal Potency Predictor, to forecast the efficacy of antifungal molecules based on their chemical structures, aiming to accelerate crop protection product development.
AgPlenus Ltd., an Israeli company specializing in crop protection products and a subsidiary of Evogene Ltd., announced the launch of its Antifungal Potency Predictor (APP) AI model. This model predicts the antifungal potency of small molecules directly from their chemical structures, enhancing the capabilities of Evogene's ChemPass AI for Ag™ platform by forecasting biological efficacy before synthesis and testing.
Fungal diseases cause significant crop losses globally, leading to tens of billions of dollars in economic damage annually and threatening food security. The widespread use of existing fungicides has also led to increased resistance, creating an urgent need for novel fungicides with new modes of action.
The APP model utilizes advanced machine learning algorithms trained on AgPlenus' proprietary datasets. It forecasts antifungal potency in the earliest discovery stages, allowing for informed decisions before chemical synthesis and biological testing. This approach is designed to reduce the number of molecules requiring experimental evaluation and focus resources on candidates with a higher probability of success.
The new model is expected to advance AgPlenus' internal fungicide pipeline, including the development of the APTF-1 target for combating Septoria Wheat Blotch. It also aims to support pipeline expansions targeting other critical pathogens. AgPlenus and Evogene plan to co-develop additional predictive AI models based on this technology.
AgPlenus is focused on developing novel, sustainable crop protection solutions. Its parent company, Evogene, is a leader in computational chemistry and AI-driven design of small molecules for pharmaceutical and agricultural applications.