STOCK TITAN

AgPlenus Launches Novel AI Model for Predicting Antifungal Potency, Expanding ChemPass AI for Ag™ Capabilities

(Positive)
Tags
AI

AgPlenus, a subsidiary of Evogene (NASDAQ: EVGN), launched its Antifungal Potency Predictor (APP), a machine-learning model that predicts antifungal potency of small molecules directly from chemical structure. The model expands ChemPass AI for Ag™ by forecasting biological efficacy before synthesis and fungal assays.

According to AgPlenus, APP should reduce the number of molecules needing experimental testing, prioritize candidates with higher probability of success, and accelerate fungicide discovery. It is intended to support AgPlenus’ fungicide pipeline, including target APTF-1, and future programs against pathogens such as Botrytis and Fusarium.

Loading...
Loading translation...

Positive

  • None.

Negative

  • None.

Market reaction after AI model launch for antifungal discovery: EVGN +19.95% in the Jul 15 session

+19.95%
22 alerts
+19.95% Session close to close
+17.4% Peak Tracked
-8.8% Trough Tracked
$5.85M Market Cap
0.2x Rel. Volume

In the Jul 15 session, EVGN gained 19.95%, reflecting a significant positive market reaction. Argus tracked a peak move of +17.4% during that session. Argus tracked a trough of -8.8% from its starting point during tracking. Our momentum scanner triggered 22 alerts that day, indicating elevated trading interest and price volatility.

Data tracked by StockTitan Argus on the day of publication.

Market Context

The stock surged +19.9% in the session following this news. If shares moved sharply higher, investor...
Analysis

The stock surged +19.9% in the session following this news. If shares moved sharply higher, investors might be keying off the $22 billion fungicide market and Evogene’s record of large upside on prior AI news, including one event that doubled the stock. Low short interest tempers short-squeeze risk but active shelf capacity adds dilution overhang.

Key Figures

Global fungicide market: $22 billion
1 metrics
Global fungicide market $22 billion Estimated annual fungicide market size

Previous AI Reports

4 past events · Latest: Jul 08 (Positive)
Same Type Pattern 4 events
Date Event Sentiment 24h Move Catalyst
Jul 08 AI collaboration Positive -2.5% Joint initiative with BCDD to accelerate AI-driven small-molecule drug discovery.
Feb 17 AI collaboration Positive +1.7% Collaboration with QUT to apply ChemPass AI to cancer therapeutics discovery.
Jun 10 AI platform update Positive +100.0% Completion of first-in-class generative AI foundation model for molecule design.
Oct 31 AI collaboration Positive -5.0% Collaboration with Google Cloud to develop generative AI model for small molecules.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Pattern Detected

AI-related announcements have produced mixed reactions, with both sharp gains and notable declines following prior AI news.

Key Terms

machine learning, modes of action, chemical synthesis
3 terms
machine learning technical
"This new machine learning model predicts the antifungal potency"
Machine learning is a set of computer programs that learn patterns from large amounts of data and improve their predictions or decisions over time, like a recipe that gets better each time it’s adjusted based on taste tests. For investors it matters because these systems can speed up analysis, spot trends or risks humans might miss, automate routine work, and potentially create competitive advantages or cost savings that affect a company’s performance.
View in glossary
modes of action medical
"need for novel fungicides with innovative modes of action (MoAs)"
Modes of action describe the specific way a drug, chemical, or treatment produces its effect in the body — essentially the biological mechanism or pathway it uses, like which molecule it targets or which cell process it alters. For investors, understanding a product's mode of action matters because it indicates how likely the treatment is to work, whether it fills an unmet need, and how it might compete with or complement other therapies, similar to knowing how different tools solve the same problem.
chemical synthesis technical
"forecasting biological efficacy prior to chemical synthesis and fungal assay"
Chemical synthesis is the laboratory or industrial process of creating specific molecules by combining and transforming simpler chemical building blocks, like following a recipe to bake a cake but for chemicals. It matters to investors because how easily and cheaply a substance can be synthesized affects production costs, supply reliability, patent strength and regulatory approval, all of which influence a company’s ability to make and sell chemical or pharmaceutical products profitably.

AI-generated analysis. How Rhea-AI works. Not financial advice.

See more from StockTitan in Google Search and AI answers. Adds StockTitan as a preferred source · opens Google
Add on Google

New AI model enables ChemPass AI for Ag™ to identify and prioritize antifungal molecules with a higher probability of biological success at early discovery stages

REHOVOT, Israel, July 15, 2026 /PRNewswire/ -- AgPlenus Ltd., a company developing novel, sustainable crop protection products and a subsidiary of Evogene Ltd. (NASDAQ: EVGN) (TASE: EVGN), today announced the launch of its Antifungal Potency Predictor (APP). This 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.

AgPlenus Logo

 

Evogene Logo

The global fungicide market is estimated at approximately $22[1] billion annually. Fungal diseases cause significant crop loss worldwide, resulting in tens of billions of dollars in economic damage each year and posing a growing threat to global food security[2]. Concurrently, the widespread and repetitive use of existing fungicides has accelerated the emergence of resistant fungal pathogens, diminishing the long-term efficacy of many commercial products. As resistance continues to spread, the agriculture industry faces an urgent need for novel fungicides with innovative modes of action (MoAs) and distinct chemical structures.

The APP model, developed using advanced machine learning algorithms trained on AgPlenus' proprietary curated datasets, represents a significant milestone in the company's AI-driven fungicide discovery capabilities. Building upon the proven success of the ChemPass AI for Ag™ platform in identifying novel crop protection targets and generating molecules with high target-protein affinity, the new model further extends these capabilities to predict the small molecule activity within the fungus itself.

By forecasting antifungal potency during the earliest discovery phases, the model enables highly informed decision-making prior to chemical synthesis and biological testing. This approach significantly reduces the number of molecules requiring experimental evaluation, focusing resources on candidates with the highest probability of downstream development success and accelerating the discovery of next-generation fungicides.

The launch of the APP model is expected to support and advance AgPlenus' internal fungicide pipeline, which includes promising targets such as APTF-1. This target is designed to combat devastating global crop diseases, including Septoria Wheat Blotch. Additionally, the model is expected to contribute to planned pipeline expansions targeting other critical pathogens, such as Botrytis and Fusarium.

Beyond its immediate application in accelerating current and future product development, the APP model lays the groundwork for additional predictive AI models that AgPlenus and Evogene plan to co-develop, aiming to forecast other critical biological attributes throughout the crop protection discovery process.

Dr. Dan J. Gelvan, CEO of AgPlenus, commented: "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."

[1] Based on Company's calculations.
[2] https://www.mpg.de/23545750/plants-fungal-diseases 

About Evogene Ltd.

Evogene Ltd. (NASDAQ: EVGN) (TASE: EVGN) is a pioneering company in computational chemistry specializing in the generative design of small molecules for drug development and ag-chemical products. At the core of its technology is ChemPass AI™ a proprietary generative AI designed to explore vast chemical space and generate novel, highly potent small molecules optimized across multiple critical parameters. Built on this powerful technological foundation, and through strategic partnerships alongside internal product development, Evogene is focused on products for the pharmaceutical and agricultural industries, driven by the integration of scientific innovation with real-world industry needs.

For more information, please visit www.evogene.com.

About AgPlenus Ltd.

AgPlenus, a subsidiary of Evogene, is a platform company designing effective and sustainable crop protection products. At AgPlenus, we are solving pesticide resistance by infusing the discovery process with predictive biology and artificial intelligence. AgPlenus leverages the ChemPass AI™ tech-engine, licensed by Evogene, to discover and bring to market effective and sustainable crop protection products. Our target-based approach allows us to reduce risk and increase efficiency, so that we can deliver on our promise to defeat global pesticide resistance.

For more information, please visit www.agplenus.com.

Forward-Looking Statements

This press release contains "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995 relating to future events. These statements may be identified by words such as "may," "could," "expects," "hopes," "intends," "anticipates," "plans," "believes," "scheduled," "estimates," "demonstrates", "designed to," "intended to," "with the goal of," or words of similar meaning. For example, Evogene and its subsidiaries use forward-looking statements in this press release when it discusses: the Antifungal Potency Predictor's ability to forecast biological efficacy prior to chemical synthesis and fungal assay validation and to predict the small molecule activity within the fungus itself, the ability to reduce the number of molecules requiring experimental evaluation, focusing resources on candidates with the highest probability of downstream development success and accelerating the discovery of next-generation fungicides, the new model's contribution to planned pipeline expansions and APP model ability to lay the groundwork for additional predictive AI models. Such statements are based on current expectations, estimates, projections and assumptions, describe opinions about future events, involve certain risks and uncertainties which are difficult to predict and are not guarantees of future performance. Therefore, actual future results, performance or achievements of Evogene and its subsidiaries may differ materially from what is expressed or implied by such forward-looking statements due to a variety of factors, many of which are beyond the control of Evogene and its subsidiaries, including, without limitation, the aftermath of the recent war between Israel and each of (i) the terrorist groups, Hamas and Hezbollah, (ii) Iran, and (iii) other regional terrorist groups supported by Iran, and any potential destabilizations in Israel, neighboring territories or the Middle East region, and those risk factors contained in Evogene's reports filed with the applicable securities authority. In addition, Evogene and its subsidiaries rely, and expect to continue to rely, on third parties to conduct certain activities, such as their and pre-clinical studies, and if these third parties do not successfully carry out their contractual duties, comply with regulatory requirements or meet expected deadlines, Evogene and its subsidiaries may experience significant delays in the conduct of their activities. Evogene and its subsidiaries disclaim any obligation or commitment to update these forward-looking statements to reflect future events or developments or changes in expectations, estimates, projections and assumptions.

Investor Relations Contact:

ir@evogene.com
Tel: +972-8-9311901

Logo: https://mma.prnewswire.com/media/1947468/Evogene_Logo.jpg
Logo: https://mma.prnewswire.com/media/1334691/AgPlenus_Logo.jpg

 

Cision View original content:https://www.prnewswire.com/news-releases/agplenus-launches-novel-ai-model-for-predicting-antifungal-potency-expanding-chempass-ai-for-ag-capabilities-302826355.html

SOURCE AgPlenus Ltd.

FAQ

What did AgPlenus and Evogene (NASDAQ: EVGN) announce on July 15, 2026 about antifungal AI?

AgPlenus announced the launch of its Antifungal Potency Predictor (APP) AI model on July 15, 2026. According to AgPlenus, APP predicts antifungal potency directly from chemical structure, expanding the ChemPass AI for Ag platform and helping prioritize molecules earlier in the fungicide discovery process.

How does AgPlenus’ Antifungal Potency Predictor AI model work for fungicide discovery at EVGN?

The APP model uses advanced machine-learning algorithms trained on AgPlenus’ proprietary curated datasets to predict antifungal potency from chemical structures. According to AgPlenus, this enables forecasting biological activity before chemical synthesis and fungal assays, reducing experimental workload and focusing resources on higher-probability candidate molecules.

How could the Antifungal Potency Predictor affect Evogene (EVGN) and AgPlenus’ fungicide pipeline?

According to AgPlenus, the APP model is expected to support and advance its internal fungicide pipeline. It is intended to prioritize molecules with higher likelihood of downstream development success and to accelerate programs around targets such as APTF-1 and planned expansions to additional fungal pathogens.

Which crop diseases and pathogens are targeted using AgPlenus’ APP-enhanced fungicide discovery for EVGN?

AgPlenus reports that its pipeline, supported by the APP model, includes the APTF-1 target for diseases like Septoria Wheat Blotch. The company also plans pipeline expansions toward major fungal pathogens such as Botrytis and Fusarium, which cause significant global crop losses.

How does ChemPass AI for Ag™ and the new APP model address fungicide resistance in agriculture?

ChemPass AI for Ag™ has been used to identify novel crop protection targets and molecules with high target-protein affinity. According to AgPlenus, integrating APP adds prediction of activity within the fungus, aiming to discover novel fungicides with innovative modes of action that may help address resistance.