Company: EVGN
Filing Date: 2025-03-27
Form Type: 20-F
Source: 0001178913-25-001092
Chunk: 58

Company: Evogene Ltd.
Filing Date: 2025-03-27
Form: 20-F
Item: Item 4
Chunk 58
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 and development of each product into pre-defined criteria, based on profound scientific understanding and know-how; second, designing genomic, chemical and microbial databases holding diverse types of curated data specifically aimed at addressing the biological challenges identified; third, developing state of the art computational tailored analytics, including artificial intelligence algorithms, designed to provide more accurate predictions to those challenges; and fourth, utilizing screening and validation systems, comprised of multiple tailored bioassays, to validate the product candidates and assist in their optimization.
 

Proprietary Databases
 
Our databases leverage multiple types of tailored big data from various sources in order to support the different research and development activities powered by our technological engines. Specifically, we focus on four different entities: microbial organisms, microbial genes, small molecules and plant genes. Our databases on different entities are rich and highly interconnected, enabling our analysis platforms to maximize their predictive power. Our databases draw in part from the public domain, and in part compile increasing amounts of proprietary data, generated either in-house or received from our collaborators.
Discovery and Development Engines
 
The CPB platform is the foundation for Evogene’s three technological engines boosting the discovery and development of novel life science products. At the core of our engines are unique computational analysis platforms, which are comprised of algorithms designed to address a vast number of parameters required for a product. These computational analysis platforms, which increasingly utilize artificial intelligence, machine learning driven approaches and other sophisticated algorithms, are designed to deliver innovative solutions to key bottlenecks in the product development process, such as efficacy and stability. As our predictions undergo validation via dedicated validation systems, we continuously improve our predictions by feeding back some of these results into our systems.
 
MicroBoost AI employs an innovative function-based approach based on a proprietary microbial function catalog for the identification of novel microbial candidates. This engine not only aims to identify candidates with high potential for a specific product, but also pinpoints the biological reasoning behind its selection, improving the chances of the initial microbial candidate to pass the subsequent optimization and development phases.
 
ChemPass AI combines a large, well-organized, database of over 30 billion known molecules as well as a set of AI-based algorithms and innovative chemo-informatics tools which invent, prioritize and analyze new small molecules prior to their expensive synthesis and testing phase. This platform is used to drive and accelerate the small molecule product development process by using a set of high-end, validated tools and algorithms for virtual screening for the identification of small molecule hits meeting multiple end-product attributes.
 
GeneRator AI aims to develop life science products via targeting