VICTORIA, British Columbia--(BUSINESS WIRE)--IPA (IMMUNOPRECISE ANTIBODIES LTD.) (the “Company” or “IPA”) (NASDAQ: IPA), an artificial intelligence-driven biotherapeutic research and technology company, today announced the development of a Foundation AI Model that represents a significant advancement in life sciences research and development.

不列颠哥伦比亚省维多利亚市--（商业新闻短讯）--人工智能驱动的生物治疗研究和技术公司IPA（IMMUNOPRECISE ANTIBODIES LTD.）（“公司”或“IPA”）（纳斯达克：IPA）今天宣布开发一种基金会AI模型，该模型代表了生命科学研究和开发的重大进步。

The Company’s model uniquely combines the strengths of Large Language Models (LLMs) through an advanced stacking technique with BioStrand's patented HYFT Technology. The HYFT's ability to pinpoint unique 'fingerprints' in biological sequences enables the stacked LLMs to apply their vast knowledge base with greater specificity, leading to more accurate predictions and insights.

该公司的模型独特地结合了大型语言模型（LLM）的优势，通过先进的堆叠技术和BioStrand的专利HYFT技术。HYFT能够精确定位生物序列中独特的“指纹”，使堆叠的LLM能够以更高的特异性应用其庞大的知识库，从而产生更准确的预测和见解。

This integration marks a pivotal moment in the utilization of artificial intelligence for complex biological data analysis and drug discovery..

这种整合标志着利用人工智能进行复杂生物数据分析和药物发现的关键时刻。。

Unveiling the Intricacies of HYFT Technology

揭示HYFT技术的复杂性

Central to the success of BioStrand's Foundation AI Model is its utilization of its patented HYFT technology, a sophisticated framework designed to identify and leverage universal fingerprint™ patterns across the biosphere. These fingerprints act as critical anchor points, encompassing detailed information layers that bridge sequence data to structural data, functional information, bibliographic insights, and beyond, serving as the great connector between disparate realms of knowledge.

BioStrand基金会人工智能模型成功的关键是它利用了其获得专利的HYFT技术，这是一种复杂的框架，旨在识别和利用整个生物圈的通用指纹™模式。这些指纹充当关键的锚点，包含将序列数据连接到结构数据、功能信息、书目见解等的详细信息层，是不同知识领域之间的重要连接点。

BioStrand’s platform core is built upon a comprehensive and continuously expanding knowledge graph, mapping 25 billion relationships across 660 million data objects, and linking sequence, structural, and functional data from the entire biosphere to written text such as scientific literature, providing a holistic understanding of the relationships between genes, proteins, and biological pathways..

BioStrand的平台核心是建立在一个全面且不断扩展的知识图上，在6.6亿个数据对象上绘制250亿个关系图，并将整个生物圈的序列、结构和功能数据链接到科学文献等书面文本，从而全面了解基因、蛋白质、，和生物途径。。

The seamless integration of HYFTs with stacked LLMs enables the BioStrand AI model to decode the complex language of proteins, unlocking insights crucial for antibody drug development and precision medicine.

HYFTs与堆叠LLM的无缝集成使BioStrand AI模型能够解码蛋白质的复杂语言，从而解开对抗体药物开发和精准医学至关重要的见解。

Large Language Models (LLM), originally developed for Natural Language Processing (NLP), can also be applied on “the language of proteins” enabling insights into tasks including, but not limited to, protein structure prediction, antibody binding optimization, and protein mutagenesis.

最初为自然语言处理（NLP）开发的大型语言模型（LLM）也可以应用于“蛋白质的语言”，从而能够深入了解任务，包括但不限于蛋白质结构预测，抗体结合优化和蛋白质诱变。

To understand ‘the language of proteins’, it is essential to detect meaningful words and word boundaries. This is where the HYFTs serve as critical enablers. By harnessing HYFT's sophisticated computational capabilities, the previously abstract notion of identifying functional units or 'words' in protein sequences is made tangible, allowing for precise mapping and analysis..

要理解“蛋白质的语言”，必须检测有意义的单词和单词边界。这是HYFTs发挥关键作用的地方。通过利用HYFT复杂的计算能力，以前抽象的识别蛋白质序列中功能单元或“单词”的概念变得有形，从而可以进行精确的映射和分析。。

The Advanced Foundation AI model employs a distinctive approach known as 'LLM stacking' to intelligently combine different LLMs, with the HYFTs linked to specific features found in various LLMs. Using a natural language analogy, this would mean one is able to distinguish the meaning of ‘apple’ based specifically on the context of the word, in other words, is the word “apple” referring to a type of fruit versus ‘Apple’, Silicon Valley pioneer.

Advanced Foundation AI模型采用了一种独特的方法，称为“LLM堆叠”，以智能地组合不同的LLM，其中HYFT与各种LLM中发现的特定功能相关联。使用自然语言类比，这意味着人们能够根据单词的上下文来区分“苹果”的含义，换句话说，“苹果”一词是指一种水果还是硅谷先驱“苹果”。

In a life sciences context, these features, for example, could include identification of critical amino acid residues involved in protein binding or detecting sequence variations associated with disease susceptibility. The sequence diversity harnessed by the HYFTs was discovered during the clustering of Next Generation Sequencing data sourced from IPA’s pipeline subsidiary, Talem Therapeutics, utilizing the HYFT network combined with LLM stacking.

例如，在生命科学的背景下，这些特征可能包括鉴定与蛋白质结合有关的关键氨基酸残基或检测与疾病易感性相关的序列变异。在对来自IPA管道子公司Talem Therapeutics的下一代测序数据进行聚类期间，利用HYFT网络结合LLM堆叠，发现了HYFT利用的序列多样性。

Through the incorporation of various features provided by LLM stacking in this study, it was possible to differentiate between binding and non-binding antibodies, even when they shared similar HYFT patterns..

通过在本研究中结合LLM堆叠提供的各种功能，即使它们具有相似的HYFT模式，也可以区分结合抗体和非结合抗体。。

Pioneering a New Frontier in Life Sciences

开创生命科学的新前沿

The concept of 'word boundaries' within protein languages offers a groundbreaking approach to unlocking the complexities of protein structure and function, filling a void in the knowledge base of researchers and drug developers alike. By enabling precise identification and manipulation of functional units within proteins, this innovative methodology paves the way for advancements in drug discovery, protein-based therapeutics, and synthetic biology.

蛋白质语言中的“单词边界”概念为解开蛋白质结构和功能的复杂性提供了一种开创性的方法，填补了研究人员和药物开发人员知识库的空白。通过精确识别和操纵蛋白质中的功能单元，这种创新的方法为药物发现，基于蛋白质的疗法和合成生物学的进步铺平了道路。

It promises not only to accelerate the development of targeted treatments with higher efficacy and lower side effects but also to revolutionize protein engineering and design. This approach, leveraging cutting-edge computational models and analysis techniques, stands to significantly reduce research and development timelines and costs ..

它不仅有望加速开发具有更高功效和更低副作用的靶向治疗，而且有望彻底改变蛋白质工程和设计。这种方法利用了尖端的计算模型和分析技术，可以大大减少研发时间表和成本。。

Advancing Drug Discovery and Precision Medicine - LENSai ™ Integrated Intelligence Technology™

推进药物发现和精准医学-LENSai™集成智能技术™

This methodology revolutionizes biotechnology and pharmaceutical research by providing a robust framework for drug discovery, protein engineering, and the development of protein-based therapeutics. The HYFT technology’s application of 'word boundaries' is particularly compelling, as it aims to significantly accelerate research and development processes.

这种方法通过为药物发现，蛋白质工程和基于蛋白质的疗法的开发提供强大的框架，彻底改变了生物技术和药物研究。HYFT技术对“单词边界”的应用尤其引人注目，因为它旨在大大加速研究和开发过程。

Through the facilitation of targeted treatments and the innovation of novel therapies, the HYFT technology offers a reduction in development timelines and costs ..

通过促进靶向治疗和创新新疗法，HYFT技术减少了开发时间表和成本。。

By providing a comprehensive understanding of the complex relationships between genes, proteins, and biological pathways, the model paves the way for the development of targeted therapies and personalized treatment strategies.

通过全面了解基因，蛋白质和生物途径之间的复杂关系，该模型为靶向治疗和个性化治疗策略的发展铺平了道路。

Reaffirming BioStrand's Leadership in Biotech Innovation

重申BioStrand在生物技术创新方面的领导地位

'The development of our Foundation AI Model, powered by our unique 'LLM stacking' approach and patented HYFT technology, marks a significant milestone in the field of biotechnological research,' stated Dirk Van Hyfte MD, PhD, Co-Founder and Head of Innovation of BioStrand. 'This innovation not only expands the boundaries of current biotech research, but also establishes a new standard for the application of AI in solving complex biological challenges.'.

BioStrand联合创始人兼创新负责人Dirk Van Hyfte医学博士表示：“我们独特的“LLM堆叠”方法和获得专利的HYFT技术推动了基础AI模型的发展，这标志着生物技术研究领域的一个重要里程碑。”这项创新不仅扩大了当前生物技术研究的范围，而且为人工智能在解决复杂生物挑战方面的应用建立了新的标准。”。

“As the global community recognizes the transformative potential of artificial intelligence in the life sciences,” Dr. Hyfte continued, “I am confident that BioStrand's Foundation AI Model will stand at the forefront of innovation and the future of AI-driven solutions in biology and drug discovery.”.

“随着全球社会认识到人工智能在生命科学中的变革潜力，”海夫特博士继续说道，“我相信，BioStrand基金会的人工智能模型将站在创新的最前沿，以及人工智能驱动的生物和药物发现解决方案的未来。”。

A Future of Collaborative Discovery

协作发现的未来

In alignment with our mission to foster collaboration and innovation within the life sciences community, we are excited to announce that IPA's CEO, Dr. Jennifer Bath, will participate in the H.C. Wainwright 1st Annual Artificial Intelligence Based Drug Discovery & Development Virtual Conference today March 7th, 2024.

为了配合我们在生命科学界促进合作与创新的使命，我们很高兴地宣布，IPA的首席执行官Jennifer Bath博士将于2024年3月7日参加H.C.Wainwright第一届基于人工智能的药物发现与开发年度虚拟会议。

This participation underscores our commitment to leading the conversation on the future of AI-driven solutions in biology and medicine..

这次参与强调了我们致力于领导关于人工智能驱动的生物学和医学解决方案未来的对话。。

Additionally, we are thrilled to announce the participation of Dirk Van Hyfte MD, PhD, Co-Founder and Head of Innovation of BioStrand, alongside our esteemed technology partner, InterSystems, at this year's HIMSS®24 conference in Orlando, Florida. Together, we will be showcasing our latest advancements in the field of healthcare technology through InterSystems’s Innovator Introduction program..

此外，我们非常高兴地宣布，BioStrand联合创始人兼创新主管Dirk Van Hyfte医学博士和我们尊敬的技术合作伙伴InterSystems将参加今年在佛罗里达州奥兰多举行的HIMSS®24会议，我们将通过InterSystems的创新者引进计划展示我们在医疗保健技术领域的最新进展。。

Our presentation will focus on introducing our groundbreaking Universal Foundation AI Model for Multiscale Biological Data Integration.

我们的演讲将重点介绍我们开创性的通用基础AI模型，用于多尺度生物数据集成。

We invite you to join us for our lightning pitch session, where we will delve into the capabilities and potential impact of our Universal Foundation AI Model. Also, we welcome you to engage in fruitful conversations at InterSystem's booth, #1361 at the HIMSS conference, March 12th-14th, 2024.

我们邀请您加入我们的lightning pitch课程，我们将深入研究我们的Universal Foundation AI模型的功能和潜在影响。此外，我们欢迎您在2024年3月12日至14日的HIMSS会议上，在InterSystem的展位上进行富有成效的对话。

About ImmunoPrecise Antibodies Ltd.

关于ImmunoPrecise Antibodies Ltd。

ImmunoPrecise Antibodies Ltd. has several subsidiaries in North America and Europe including entities such as Talem Therapeutics LLC, BioStrand BV, ImmunoPrecise Antibodies (Canada) Ltd. and ImmunoPrecise Antibodies (Europe) B.V. (collectively, the “IPA Family”). The IPA Family is a biotherapeutic research and technology group that leverages systems biology, multi-omics modelling and complex artificial intelligence systems to support its proprietary technologies in bioplatform-based antibody discovery.

ImmunoPrecise Antibodies Ltd.在北美和欧洲拥有多家子公司，包括Talem Therapeutics LLC、BioStrand BV、ImmunoPrecise Antibodies（Canada）Ltd.和ImmunoPrecise Antibodies（Europe）B.V.（统称“IPA家族”）等实体。IPA家族是一个生物治疗研究和技术小组，利用系统生物学，多组学建模和复杂的人工智能系统来支持其基于生物平台的抗体发现专有技术。

Services include highly specialized, full-continuum therapeutic biologics discovery, development, and out-licensing to support its business partners in their quest to discover and develop novel biologics against the most challenging targets. For further information, visit www.ipatherapeutics.com..

服务包括高度专业化的全连续治疗生物制剂的发现，开发和外授权，以支持其业务合作伙伴针对最具挑战性的目标发现和开发新型生物制剂。欲了解更多信息，请访问www.ipatherapetics.com。。

Forward Looking Information

前瞻性信息

This news release contains forward-looking statements within the meaning of applicable United States securities laws and Canadian securities laws. Forward-looking statements are often identified by the use of words such as “potential”, “plans”, “expects” or “does not expect”, “is expected”, “estimates”, “intends”, “anticipates” or “does not anticipate”, or “believes”, or variations of such words and phrases or state that certain actions, events or results “may”, “could”, “would”, “might” or “will” be taken, occur or be achieved.

本新闻稿包含适用的美国证券法和加拿大证券法所指的前瞻性声明。前瞻性陈述通常通过使用诸如“潜在”、“计划”、“预期”或“不预期”、“预期”、“估计”、“打算”、“预期”或“不预期”或“相信”等词语或短语的变体来识别，或声明某些行动、事件或结果“可能”、“可能”、“会”、“可能”或“将”采取、发生或实现。

Forward-looking information contained in this news release includes, but is not limited to, statements relating to the expected outcome on the market, the life sciences, drug discovery and development, integration and / or success of LENSai, LLMs, RAG, or HYFT technologies, including their benefits, and statements relating to IPA’s expected increased revenue streams and financial growth.

本新闻稿中包含的前瞻性信息包括但不限于与市场预期结果、生命科学、药物发现和开发、LENSai、LLMs、RAG或HYFT技术的整合和/或成功相关的声明，包括其收益，以及与IPA预期增加的收入流和财务增长相关的声明。

In respect of the forward-looking information contained herein, IPA has provided such statements and information in reliance on certain assumptions that management believed to be reasonable at the time..

关于本文所含的前瞻性信息，IPA根据管理层当时认为合理的某些假设提供了此类声明和信息。。

Forward-looking information involves known and unknown risks, uncertainties and other factors which may cause the actual results, performance or achievements stated herein to be materially different from any future results, performance or achievements expressed or implied by the forward-looking information.

前瞻性信息涉及已知和未知的风险、不确定性和其他因素，这些因素可能导致本文所述的实际结果、业绩或成就与前瞻性信息明示或暗示的任何未来结果、业绩或成就存在重大差异。

Actual results could differ materially from those currently anticipated due to a number of factors and risks, including, without limitation, the risk that the integration of IPA’s LENSai platform with its HYFT technology may not have the expected results, risks that the expected healthcare benefits including lowering development timeliness, and costs and that development of targeted treatments with higher efficacy and lower side effects will not be achieved, risks that the benefits to drug discovery, protein-based therapeutics, and synthetic biology won't be achieved, in addition actual results could differ materially from those currently anticipated due to a number of factors and risks, as discussed in the Company’s Annual Information Form dated July 10, 2023 (which may be viewed on the Company’s profile at www.sedar.com), and the Company’s Form 40-F, dated July 10, 2023 (which may be viewed on the Company’s profile at www.sec.gov).

由于许多因素和风险，实际结果可能与目前预期的结果存在重大差异，包括但不限于IPA的LENSai平台与其HYFT技术的集成可能无法产生预期结果的风险，预期的医疗保健效益（包括降低开发及时性）的风险，成本和开发具有更高疗效和更低副作用的靶向治疗将无法实现，药物发现，基于蛋白质的治疗和合成生物学的益处将无法实现的风险，此外，由于许多因素和风险，实际结果可能与目前预期的结果存在重大差异，如2023年7月10日的公司年度信息表（可在www.sedar.com的公司简介中查看）和2023年7月10日的公司40-F表（可在www.sec.gov的公司简介中查看）所述。

Should one or more of these risks or uncertainties materialize, or should assumptions underlying the forward-looking statements prove incorrect, actual results, performance, or achievements may vary materially from those expressed or implied by the forward-looking statements contained in this news release.

如果出现一个或多个此类风险或不确定性，或者前瞻性声明的假设被证明不正确，则实际结果、绩效或成就可能与本新闻稿中包含的前瞻性声明所明示或暗示的结果、绩效或成就存在重大差异。

Accordingly, readers should not place undue reliance on forward-looking information contained in this news release. The forward-looking statements contained in this news release are made as of the date of this release and, accordingly,.

因此，读者不应过度依赖本新闻稿中包含的前瞻性信息。本新闻稿中包含的前瞻性声明自本新闻稿发布之日起做出，因此，。