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Hello and good day, everyone. Thank you for your interest in Evogen. We are pleased to share with you the story of our company as we continue our path to success. Evogen is a publicly traded company listed in the Nasdaq Stock Exchange in New York and on the Tel Aviv Stock Exchange under the ticker symbol EVGN. As such, we are required to present a forward-looking statement disclaimer. Please note that certain statements made during this presentation may constitute forward-looking statements and are subject to risk and uncertainties. At Evogen, we are on an ongoing mission to redefine the future of small molecule-based discovery and optimization. By harnessing the power of our proprietary generative AI tech engine, Kempas AI, we design novel groundbreaking small molecules, highly potent and precisely optimized across multiple parameters, to transform the pharmaceutical and alchemical industries. Our goal is not just innovation, but meaningful beneficial impact for our world. We call this approach real-world innovation. It all begins with our powerful engine, Kempas AI, a generative AI-first platform designed to create novel small molecules optimized simultaneously across multiple biological and chemical parameters. We deploy this technology across two sectors, pharmaceuticals and agriculture. In pharma, we focus on the discovery and optimization of innovative small molecule therapeutics. In agriculture, we apply our capabilities to development of next-generation ag chemicals. Both industries share a common and complex challenge, identifying small molecule candidates with the highest probability of advancing successfully into commercial products. To understand the unique value of Kempas AI, it's helpful to first look at the small molecule product development process and the challenges it presents. Small molecule products, such as drugs and pesticides, are typically developed through a multistage process. It begins with identifying a biological target, usually a protein whose activity needs to be inhibited. Researchers then search for a chemical molecule capable of binding to that target, selecting from an almost limitless universe of possible compounds. During the discovery and optimization phases, the primary objective is to identify the most promising candidate to advance into the latest stages of development. These downstream stages are highly time-consuming and capital-intensive, making early decision-making critically important. Once a candidate enters these advanced stages, its chemical structure is largely fixed. This is the molecule that is expected, if successful, to progress all the way to market. The outcome of the process is frustrating. Statistically, only a small fraction of promising molecules that make it into advanced development actually reach the market. Success rates are usually somewhere between 3 to 10 percent, depending on the industry. It naturally raises the question, what causes those success rates to be so low? A major barrier in small molecule discovery is the limited exploration of chemical space. Most candidates come from a narrow, well-charted domain, restricting diversity, limiting innovation, and reducing the chances of breakthrough discoveries. At the same time, commercialization requires the simultaneous optimization of multiple, often conflicting parameters, such as potency, selectivity, safety, and synthesizability. These challenges are interconnected and create a significant development bottleneck. Overcoming them represents a powerful commercial opportunity, precisely the focus of Evogen. We developed Kempas AI exactly for this, breaking chemical space limitation and multi-parameter challenge. This is our core computational platform, built to deliver on three fundamental objectives. First, generating novel, drug-like synthesizable molecules. The system is designed to access vast and explored chemical space to generate new IP while ensuring these compounds retain activity and are fundamentally synthesizable. Second, true multi-parameter optimization. We know that optimizing sequentially often leads to dead ends. Campus AI is built to balance project-specific chemical, biological, and physical constraints simultaneously. And third, de-risking downstream development. By generating a diverse, high number of candidates that hit those development criteria from day one, the goal is to significantly reduce late-stage attrition. Let's look at how we actually do this. The process begins with defining the desired product, given the target protein, moving all the way to a novel chemical molecule that meets the criteria of an optimized lead. This means designing a product candidate molecule that can either advance to clinical trials in pharma or proceed to field trials in alchemicals. The workflow includes three main steps. Step 1. We use Kempas AI to search for the most promising molecules that can bind to the target protein, screening through an ultra-large database of 70 billion commercially available compounds. These molecules are then experimentally tested in relevant assays, and the best performers move to the next step. Step 2. Around each stock-performing molecule was searched through families of purchasable similar compounds with comparable properties. These are experimentally test again in the relevant assay to refine our selection. detection. These molecules are then experimentally tested in relevant assays. Step 3. With the insights and data gathered so far, we move to the most critical step, using our generative AI engine to design entirely new molecules. These novel molecules are synthesized and representing novel chemical entities, meeting predefined product requirements, with the highest potential to successfully advance in the development process. These molecules are then experimentally tested in relevant assays, leading to a select set of candidates that meet preclinical advancement criteria or field trials and have the highest potential for commercial success as therapeutics or crop protection products. To summarize, this design process leverages Evogen's Kempass AI and the comprehensive data generated throughout the workflow. The process concludes with a refined set of product candidate molecules that meet three key criteria, novelty, alignment with the defined diverse product requirements, and high potency. Science's core advantage is its ability to translate complex biological requirements and chemical constraints into computational models that design novel molecules tailored to a specific product profile. Our discovery process combines biology, chemistry, and AI within a single integrated platform. The first stages focus on building a deep understanding of the biology and chemistry underlying the target product. By screening more than 70 billion molecules and continuously validating predictions through laboratory experiments, we generate proprietary insights and unique datasets that are difficult to obtain using conventional discovery approaches. This biological and chemical understanding, together with the proprietary datasets we create, forms the foundation for the next stage of discovery. Leveraging this foundation, LEADOPS GPT, a generative AI engine trained on curated 38 billion molecules database, designs novel and synthesizable molecules while simultaneously satisfying multiple critical parameters, including efficacy, selectivity, safety, and developability. Unlike traditional approaches that generate large numbers of molecules and then filter them, LEADOPS GPT is designed to create high-quality candidates from the outset, significantly improving efficiency and probability of success. The result is a focused set of optimized candidate molecules with the highest potential to advance into preclinical development or field trials and ultimately become commercial successful products. As part of our commitment to maintain our campus AI engine at the cutting edge of innovation, Evergen collaborates with top-tier technology partners. This is the reason we are so proud and excited to continuously collaborate with Google Cloud. Our first announced collaboration with Google cloud was successfully completed in mid-2025, resulting in a first-in-class foundation model for the generation of novel molecular product candidates addressing multiple parameters. By processing 38 billion structures, we tripled the industry standard for accuracy, delivering 90% design precision. Building on this progress, we were pleased to announce our second collaboration with Google Cloud initiated this February. We are now integrating advanced AI agents into Kempas AI using Google Cloud Agent Platform, with a major focus on generating high-quality data to support multi-parameter modeling while also reducing manual errors and automating complex scientific workflows. This capability is expected to accelerate and scale our design, make, test, analyze cycles, improve the commercial potential of small molecule candidates, and strengthen our ability to support future partnerships across the pharmaceutical and agriculture industries. Returning to the outline of the development process of a small molecule-based product, leveraging Kempas AI's proprietary foundation model enables us to draw and generate our candidates from an expansive chemical space. addition, these candidates are designed to meet a broad range of critical parameters required for successful product development while representing diverse chemotypes. Importantly, the use of our engine seamlessly integrates with and accelerates the overall development process. These unique capabilities ensure that the novel chemical entities that comprise Evogen's product candidates will advance to later stages with significantly higher probability of progressing all the way to successful product realization. To capture the full value of Campus AI's capabilities across both therapeutics and agriculture, a business strategy is designed to maximize potential while minimizing risk. We are partnering with domain experts, including leaders in pharma, agrochemicals, biotech and academia to form highly strategic collaboration agreements. Through these alliances, we will co-develop innovative, highly optimized products. The expected financial upside for Evergen stems from upfront R&D fees, milestone payments, and revenue sharing mechanisms upon commercialization. I would like to emphasize that campus is built on a fully integrated partnership driven workflow which is expressed in collaborations and in-house development toward a proprietary product candidate at every step our partner is actively involved each stage begins with a joint strategic review and all computational outputs undergo rigorous experimental validation whether it is performed by our partner or in our in-house facilities, and all the results are evaluated collaboratively. Each project is uniquely tailored, ensuring that our methodologies are adapted to the specific needs of every engagement. Such an approach also provides high technological flexibility, enabling the execution of diverse project types from various starting points, for instance, who may optimize existing molecule or start a discovery from scratch. Before we move into more detailed discussion of companies' pharma and f-tech divisions, I would like to summarize the company's strategy, its business model, and how it is being implemented today. Campus AI is at the core of our proprietary offering and our foremost competitive edge. In pharma, powering the creation of novel small molecule therapeutics, In agriculture, enabling the development of next-generation crop protection solutions. To bring this vision to life, we operate through two synergistic business arms. Evogen's Pharma Division, advancing AI-driven pharmaceutical innovation, and AgPlanis, our wholly-owned subsidiary, delivering cutting-edge agricultural chemistry. Across both domains, we pursue product development either independently or in collaboration with leading multinational companies and top academic institutions, always aligning partnerships with the strategic needs of each program. These initiatives and future ones are expected to generate cash inflows for the company through upfront payments, R&D reimbursement, and, as our products advance through development, milestone payments and potential royalties. We look forward to providing future updates on both collaborative efforts and internal pipeline progress. Now, we would like to present the implementation of Evogen's technology and business model in more detail through our pharma and agriculture activities, starting with the pharma industry. First, I would like share our perspective on the opportunity in the phharma industry. Small molecule-based drugs, valued at approximately $780 billion, represent nearly 60% of the global pharmaceutical market. Even more exciting is the current momentum of AI-designed small molecules that are advancing through various companies' pipelines, more than 60 new candidates with an expected annual growth rate exceeding 150%. This rapid expansion is expected to drive the AI drug discovery market to nearly $190 billion by 2034. Looking at the current pipeline landscape of AI-first drug discovery, the rapid growth is clear. With a robust pipeline of assets advancing through the clinical trials, the arrival of the first AI-designed drugs on the market is truly just a matter of time. Our vision is for Evogen to join this group of leaders, either through strategic partnerships or by advancing its own pipeline to a clinical development and commercialization. As discussed earlier, our partnership strategy is designed to maximize the value of Campus AI by co-developing innovative, highly optimized products while minimizing development risk. In the pharma industry, we identify three key segments of potential partners. One, academic spin-outs and early-stage biotech companies. 2. Established biotech and small pharma companies 3. Large, fully integrated pharmaceutical companies Each group has distinct needs across the small molecule discovery and optimization process. For example, early-stage biotechs typically bring strong biological expertise, such as target identification, but often lack advanced computational chemistry capabilities. In contrast, large pharma companies may face challenges with specific target classes that require novel computational approaches and scientific innovation. Evogen's platform is uniquely positioned to address the distinct needs of each of these market segments. Given the different needs of the market segments, the structure of the collaboration and its commercial terms will vary accordingly. However, such agreements will typically include shared ownership of collaboration outputs and participation in downstream value creation, including potential revenues generated by the final product. As we showed, throughout 2025 and early 2026, Evogen has initiated several collaborations for diverse indications with various partners in which Campus AI already identified candidates that are being experimentally validated. Translating this collaborative strategy into actual products brings us to our diversified small molecule pipeline, reflecting the robustness of KEMPASS-AI. We are advancing multiple programs across oncology, immunology, neurology, and metabolism, with our two lead assets already reaching the hit-to-lead stage. In the next slide, I would like to show results from two such programs that will show why we are confident in our technology. First, I'd like to present recent data from one of our external collaborations with a leading academic institute. This program targets novel therapies for breast cancer, focusing on a highly lucrative commercial opportunity. The global breast cancer market is massive, valued at $30 to $37 billion annually. Our collaboration specifically targets aggressive, treatment-resistant forms of the disease. This segment represents 10 to 15% of all cases, translating to a highly underserved addressable market of $1 to $5 billion. By combining our proprietary Kempas AI engine with world-class academic expertise, we are designed to rapidly generate novel small-molecule drug candidates optimized to treat these aggressive cancers. This partnership is a prime example of how we leverage our AI platform to capture high-value, unmet market segments and drive significant value into our therapeutic pipeline. Let's look at how Campus AI performs on this highly challenging project. Typically, finding starting points for a novel MOA requires a massive high-throughput screening campaign. Here, using Campus AI and our computational chemists, we identified approximately 100 highly promising virtual hits. But the real story is the experimental validation. If you look at the funnel on the right, for our intermediate analysis, we ordered and tested just a small batch of 42 compounds. Out of these 42, 18 showed clear inhibitory activity, a remarkable 43% hit rate. Even more encouraging, 8 of them, 19%, already demonstrated nanomolar potency at this early stage. You can see the quality of these hits in the assay results on the left. This isn't just binary active-inactive data. We are seeing robust dose-correlative inhibition with several compounds crossing that rigorous nanomolar threshold. As the PI on this project put it, this completely flips the traditional discovery paradigm. Instead of physically screening thousands of compounds to find a few hits, we enabled to test a fraction of the molecules while achieving significantly higher hit rates and immediate potency. In August 2025, we announced the collaboration with Professor Ehud Gazit of Tel Aviv University to develop new therapeutics for metabolic diseases linked to self-assembly of small metabolites. such as tyrosinemia and gout. These indications have the potential to address a $3 billion market segment. These indications have the potentially to address a $ 3 billion market segments. The partnership combines Evogen's Campus AI generative design engine with Professor Gazit's expertise in molecular self-assembly to discover and optimize noble small molecules that can inhibit harmful metabolite aggregation. This collaboration aims to accelerate the development of first-in-class therapies that address the underlying molecular causes of accumulated metabolic diseases, offering new hope for patients worldwide. As you will see in the next slide, initial results look promising. Let's see how Campus AI performs on this highly challenging project. This is the initial experimental data in this collaboration. Typically, finding starting points for a novel mode of action requires a massive high-throughput screening campaign. Here, using Campus AI and our computational chemists, we identified approximately 100 highly promising virtual hits. But the real story is in the experimental validation. If you look at the graph on the left, an intermediate analysis of the first 15 compounds ordered and tested shows that 33% of the compounds have clear inhibitory activity. While additional testing is ongoing, we have already secured an initial starting point, ensuring the NEST DMTA cycles will be more efficient. Now we will continue with our efforts to capture the value of our tech engine Campus AI in the ag industry, a field we entered back in 2018 through the establishment of our subsidiary, AgPlanus. Since then, AgClanus achieved significant milestones, including strategic collaborations with leading industry players such as Bayer and Corteva. Agriculture is a huge global market, valued in 2024 at $88 billion, constituting three main segments, herbicides, insecticides, and fungicides. A single product in this space can generate anywhere from hundreds to millions to billions of dollars in sales. The industry is in great need of new products, yet developing them comes with significant challenges. An increase in pest resistance and regulatory requirements An urgent need for new modes of action And the decreased rate in discovery of new pesticides due to lack of innovation The agrochemical market is largely controlled by a small group of global industry leaders including Bayer, Syngenta, Corteva, BASF, and FMC Each of these companies is a potential strategic partner for the co-development of innovative products powered by our proprietary technology platform, Kempas AI. At present, the company is pursuing two key initiatives. A collaborative program with Corteva to develop a herbicide based on a novel mode of action. An in-house program focused on developing a fungicide against the fungal pathogen responsible for substantial losses in wheat production. The company's primary focus is currently on the internal development program, which is progressing according to plan and meeting our expectations. We believe the program is approaching a state at which a strategic partnership with one of the industry's leading companies for co-development would be both timely and beneficial. I would like to provide additional details regarding this program. Zymoceptoria tritissi is a fungus causing major damage to field crops, especially wheat. We are disrupting a $1.2 billion problem. problem. 70% of EU fungicide use in wheat targets wheat blotch, representing a market exceeding $1.2 billion annually. At the same time, widespread resistance to leading products like strobilurins, despite $4.76 billion in 2025 sales, signals an urgent need for a new solution. We are pleased to report that within this program we have successfully completed the third stage of the KEMPASS-AI driven discovery process. To date, the company has generated 15 novel, innovative small molecule candidates that have demonstrated promising activity in laboratory essays. Several of these candidates have now progressed to advanced biological testing to further evaluate and validate their efficacy in plants. In this project, we focused on developing novel mode of action fungicides against Democeptoria, a fungus that causes major damage to field crops, especially wheat. Our starting point in this program was not promising since there was no available structural data on the target protein, which is a common issue in agriculture. We used homology modeling and structure-based pharmacophore development to characterize the active site. With POINTHIT, 440 candidates were selected for testing. Of these, only 11 met the enzymatic inhibition threshold and only two demonstrated antifungal activity. From these two, in vitro and in vivo validated compounds and the negative results, using active search, we selected 164 off-the-shelf molecules for purchase. Now, 38 of these showed enzymatic inhibition, and 5 demonstrated antifungal activity, showing clear improvement over the initial screen. Building on these insights, we moved to lead-up GPT and generated 27 novel compounds, which were custom synthesized and tested. Of these, 25 met the enzymatic inhibition threshold and 15 also showed the desired biological activity, which represents a dramatic improvement. This progression from two hits to 15 illustrates how the integration of iterative experimental validation with AI-driven molecular design can transform limited early signals into a focused, high-quality lead set. We look forward to continuing to update on the expansion of our crop protection pipeline and on the progress of our existing programs. Looking ahead, we anticipate meaningful progress across all three of the company's core areas of activity, reinforcing our growth trajectory and long-term value creation. Technology engine, we continue to strengthen our competitive edge through the expansion of additional technological collaborations designed to further enhance our innovative capabilities and sustain our unique market advantage. Drug Development for the Pharmaceutical Industry Advancing our existing pipeline toward key value-creating milestones Establishing new strategic collaborations with leading biotech companies and academic institutions Deepening relationships with global pharmaceutical companies Actively evaluating new opportunities to establish our internal drug development pipeline Ag chemical development for the agriculture industry Continued advancement of our existing pipeline assets Formation of new collaborations with leading ag chem companies Ongoing evaluation of opportunities to expand and strengthen our internal pipeline Overall, we remain strongly focused on execution, partnership expansion and pipeline development across all business areas, positioning the company for sustained growth and long-term success. As we reach the final slide of today's presentation, I would like to summarize the key messages. At the heart of Evogen's business is our proprietary technology engine, Kempas AI. This powerful platform enables us to generate highly potent, novel small molecules that are optimized across multiple parameters for both the pharmaceutical and agrochemical industries. To maximize the value of our technology and capture its full commercial potential, we are building a diversified pipeline of small molecule products spanning human health and agriculture. Our strategy is designed to balance value creation with risk management. Some programs will be developed and funded entirely by Evergen, allowing us to retain maximum upside. At the same time, a significant portion of our pipeline will be advanced through strategic collaborations with industry leaders that possess complementary expertise, capabilities, and market access. We believe that these partnerships create a powerful win-win opportunity. Our partners gain access to breakthrough innovation generated by Campus AI, while Evogen accelerates development, expands market reach, and creates additional value for shareholders. The progress we have demonstrated today provides further validation of both our technology and our business model, and we look forward to updating you on the important milestones and developments we expect to achieve in the coming months. This is Evogen, combining cutting-edge AI with deep scientific expertise to generate real-world innovation and transform discovery into valuable products. Thank you for your attention and continued support. We thank you for you time and we look forward to updating you on future achievements.