Evogene Announces Completion of First-In-Class Foundation Model for Generative Molecule Design, Developed in Collaboration with Google Cloud

The new model addresses the challenge of identifying novel small molecules that meet multiple product criteria, an essential requirement for pharma and agriculture applications

In both pharma and agriculture, successful product development depends on identifying molecules that meet complex performance criteria while also being patentable. Traditional discovery methods typically address these challenges sequentially, a process that reduces success probability. In addition, they tend to steer towards well-explored or saturated areas of chemical space. This limits the potential for innovation, making it difficult to secure robust intellectual property and achieve meaningful product differentiation.

In contrast, generative AI models support companies in their small molecule discovery process by enabling the simultaneous consideration of multiple complex product requirements, all while creating truly novel molecular structures. This approach also facilitates the development of strong, defensible IP portfolios. Evogene's first-in-class foundation model is designed to do exactly that.

Developed in-house by Evogene's algorithm teams, this proprietary foundation model marks a dramatic advance over traditional generative AI. Internal computational analysis shows that it delivers approximately 90% precision in successful and precise, novel molecule designs (vs. approximately 29% in traditional GPT AI-model), ensuring that each compound simultaneously meets essential parameters. This breakthrough paves the way for creating highly potent, synthesizable, and patentable molecules across life-science products.

Evogene also announces that development is already underway on version 2.0 of its generative AI foundation model, with a focus on enhanced flexibility for multi-parameter optimization. The updated version will incorporate predefined, customized parameters tailored to therapeutic contexts or specific agriculture requirements. It will enableChemPass AIto better balance complex real-world constraints, such as efficacy, toxicity, and stability, significantly improving its ability to generate molecules optimized for clinical, commercial, and regulatory success.

Evogene welcomes continued engagement with partners across the pharmaceutical and agriculture industries interested in accessing or integratingChemPass AIfor next-generation product development.

Evogene Ltd. (NASDAQ:EVGN) (TASE: EVGN) is a computational biology company leveraging big data and artificial intelligence, aiming to revolutionize the development of life-science based products by utilizing cutting-edge technologies to increase the probability of success while reducing development time and cost.

Evogene established three unique tech-engines –MicroBoost AI, ChemPass AI and GeneRator AI. Each tech-engine is focused on the discovery and development of products based on one of the following core components: microbes (MicroBoost AI), small molecules (ChemPass AI), and genetic elements (GeneRator AI).

Evogene uses its tech-engines to develop products through strategic partnerships and collaborations, and its four subsidiaries including:

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

This press release contains "forward-looking statements" relating to future events. These statements may be identified by words such as "may", "could", "expects", "hopes" "intends", "anticipates", "plans", "believes", "scheduled", "estimates", "demonstrates" or words of similar meaning. For example, Evogene and its subsidiaries are using forward-lookingstatementsin this press release when it discusses the ability of the AI foundation model to identify novel small molecules that meet multiple complex product criteria while also being patentable, the ability of the AI foundation model to create highly potent, synthesizable, and patentable molecules across life-science products, the ability of the AI foundation model to reduce late-stage failure in pharma to and develop ag-chemicals that are effective, sustainable, and proprietary, and the development of version 2.0 of Evogene's generative AI foundation model, with a focus on enhanced flexibility for multi-parameter optimization.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 current war between Israeland Hamas and any worsening of the situation inIsraelsuch as further mobilizations or escalation in the northern border ofIsraeland 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 field-trials 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.