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ImmunoPrecise Validates LENSai Epitope Mapping Platform Across Broad Range of Unseen Therapeutic Targets Beyond Training Data

Article content New benchmark confirms LENS ai 's ability to accurately predict binding on 17 previously unseen antibody-antigen complexes, achieving near-crystallography precision without prior training data. Article content AUSTIN, Texas — ImmunoPrecise Antibodies Ltd. (NASDAQ: IPA) ('IPA' or the 'Company'), an AI-powered biotherapeutics company, today announced a new validation study supporting the generalizability of its proprietary epitope mapping platform, LENS ai, powered by IPA's patented HYFT® technology. The newly released benchmark shows that the platform consistently delivers high predictive performance, even on complexes not used during training. Article content 'It's generally assumed that AI can only make accurate predictions if it has seen similar data before,' said Dr. Jennifer Bath, CEO of ImmunoPrecise. 'But this benchmark proves otherwise: LENS ai accurately mapped antibody binding sites on entirely new antibody – protein complexes-none of which were used in training. Not the antibodies. Not the targets. Not the complexes. And the predictions aligned with wet-lab results. This is a major breakthrough in generalizing AI for therapeutic discovery, made possible by our proprietary technology, which captures functional meaning instead of memorizing shapes. It shows that AI doesn't always need massive data to be powerful and accurate – it just needs the right kind.' Article content LENS ai Epitope Mapping uses artificial intelligence to pinpoint where antibodies are most likely to attach to disease-related proteins – helping scientists design better treatments faster. Unlike traditional methods that take months and require lab work, LENS ai delivers results in hours – using just the digital sequences – cutting timelines, eliminating the need to produce expensive materials, reducing guesswork, and unlocking faster paths to new treatments. Article content In a new benchmark study, LENS ai was tested on 30 antibody-protein pairs, 17 of which the platform had never seen before. Despite having no prior exposure to these molecules, LENS ai achieved prediction scores nearly identical to those from its original training data. This score, known as AUC (Area Under the Curve), is a widely accepted measure of accuracy in computational biology. Article content The consistent performance on entirely new, unseen complexes confirms that LENS ai 's artificial intelligence can reliably analyze and predict antibody binding – even for molecules outside its training set. This breakthrough demonstrates LENS ai 's power to generalize across diverse biological structures, making it a valuable tool for accelerating real-world drug discovery. Article content Why This Benchmark Matters Article content In the new study, LENS ai delivered high accuracy results on 17 antibody-protein complexes the platform had never seen before as it did on familiar training examples – proving true generalization, not memorization. Because no new wet-lab work or x-ray structures were required, researchers gain speed, reproducibility, and major cost savings, while freeing scarce lab resources for confirmatory or downstream assays. Article content What It Means for Partners and Investors Article content With LENS ai already embedded in collaborations across big pharma and biotech, ImmunoPrecise is scaling access through secure APIs and custom partnerships. The platform helps researchers compress discovery timelines, reduce risk, and unlock previously unreachable targets – positioning the company and its investors at the forefront of AI-driven antibody therapeutics. Article content For more technical detail and full benchmark results, explore two complementary case studies that illustrate the power and flexibility of LENS ai Epitope Mapping. Article content ai Article content 's breakthrough ability to accurately map binding sites on a completely Article content 'unseen' target Article content , with no prior exposure to the antibody, the antigen, or their structure. Article content These examples underscore how LENS ai performs both in well-characterized systems and in novel, previously untrained scenarios—validating its generalizability and real-world readiness. Article content About ImmunoPrecise Antibodies Ltd. Article content ImmunoPrecise (NASDAQ: IPA) is a global leader in AI-powered biotherapeutic discovery and development. Its proprietary HYFT technology and LENSai™ platform enable first-principles-based drug design, delivering validated therapeutic candidates across modalities and therapeutic areas. IPA partners with 19 of the top 20 pharmaceutical companies and is advancing next-generation biologics through data-driven, human-relevant models. Article content Forward-Looking Statements Article content This press release contains forward-looking statements within the meaning of applicable United States and Canadian securities laws. Forward-looking statements are often identified by words such as 'expects,' 'intends,' 'plans,' 'anticipates,' 'believes,' or similar expressions, or by statements that certain actions, events, or results 'may,' 'will,' 'could,' or 'might' occur or be achieved. These statements include, but are not limited to, statements regarding the performance, scalability, and broader application of the LENSai™ and HYFT® platforms; the generalizability of the Company's AI models to novel therapeutic targets; the role of AI in accelerating antibody discovery; and the Company's future scientific, commercial, and strategic developments. Article content Forward-looking statements are based on management's current expectations, assumptions, and projections about future events. Actual results may differ materially from those expressed or implied due to a variety of factors, many of which are beyond the Company's control. These factors include, but are not limited to, the pace of scientific and technological innovation, risks related to model validation and generalizability in real-world settings, intellectual property protection, strategic partner adoption, regulatory pathways, and market demand for AI-driven therapeutic platforms. Article content Forward-looking statements involve known and unknown risks, uncertainties, and other factors that could cause actual results, performance, or achievements to differ materially from those expressed or implied herein. Additional information regarding risks and uncertainties is included in the Company's Annual Report on Form 20-F, as amended, for the year ended April 30, 2024 (available on the Company's SEDAR+ profile at Article content Article content and EDGAR profile at Article content Article content ). Should any of these risks materialize, actual results could vary significantly from those currently anticipated. Article content Readers are cautioned not to place undue reliance on these forward-looking statements. Except as required by law, the Company undertakes no obligation to update or revise forward-looking statements to reflect subsequent events or circumstances. 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