IPA’s Subsidiary, BioStrand, Provides an Update on LENSai™
BioStrand Unveils Groundbreaking Retrieval Augmented Generation (RAG)-Based LLM Platform Integrated with Patented HYFT Technology
BioStrand's Work Exemplifies the IPA Family's Investment in Advanced AI, Aiding Partners in Developing New Biologics for Previously Undruggable Targets
VICTORIA, British Columbia--(BUSINESS WIRE)-- ImmunoPrecise Antibodies Ltd. (NASDAQ: IPA) (“ImmunoPrecise” or “IPA” or the “Company”), an AI-driven biotherapeutic research and technology company, announces that its subsidiary, BioStrand®, has commercially launched its state-of-the-art Retrieval Augmented Generation (RAG)-based Large Language Model (LLM) platform. This pioneering platform seamlessly integrates with the Company’s patented HYFT technology and LENSai platform, signifying a noteworthy leap in the market as the Company aims at ensuring unparalleled accuracy, interpretability, and data-centric design in generative AI tools.
BioStrand's innovative approach to solving the Information Integration Dilemma (IID) has led to the development of a unique technology design that encapsulates and unifies diverse data modalities. This includes syntactical (sequence) data, 3D structural data, unstructured scientific information (e.g., scientific literature), all integrated within a singular framework, the LENSai Knowledge Graph. This breakthrough facilitates efficient data fusion, enabling a comprehensive analysis and interpretation of complex biological data.
Knowledge Graphs and LLMs have been recognized for their superior performance over conventional approaches in drug discovery. BioStrand’s integration of their proprietary and patented technologies with LLM synergizes strengths and addresses limitations, leading to a more efficient drug discovery platform. Biomedical LLMs, specifically those pre-trained on domain-specific vocabulary, outperform traditional tools in many biological data-based tasks. For instance, for the important step of identifying drug targets, AI-powered language models have demonstrated superiority over even the most state-of-the-art approaches. Furthermore, AI-enabled LLMs are now being utilized across the drug discovery and development pipeline for predicting drug-target interactions, molecular properties, and even potential drug withdrawals due to safety concerns.
Key Features of BioStrand’s LENSai Platform:
- Holistic Integration of Data: LENSai offers a unified approach, linking sequence, structure, function, and literature information from the entire biosphere, providing a comprehensive view of life sciences data.
- Expansive Knowledge Graph: At its core, LENSai boasts a knowledge graph that maps 25 billion relationships across 660 million data objects, ensuring a deep and interconnected understanding of genes, proteins, and biological pathways.
- Neuro Symbolic Methodology: A fusion of deep learning and symbolic logic techniques, a branch of mathematics and philosophical logic that uses symbols to represent logical expressions, rather than using words. This approach harnesses the data-driven strengths of LLMs and the reasoning capabilities of symbolic systems, offering both adaptability from LLM methods and transparency from symbolic logic, ensuring comprehensive and interpretable outcomes for inquiries.
- Retrieval-Augmented Generation (RAG) Integration: LENSai utilizes RAG to enhance the accuracy of the generated responses of LLMs. By integrating with the HYFTs technology as its proprietary knowledge graph, RAG ensures that the platform provides up-to-date and factual information, reducing the chances of generating inaccurate or "hallucinated" content. This synergy is designed to allow LENSai to deliver more informed and precise answers, bridging the gap between the vast generative capabilities of LLMs and the concrete data from HYFTs technology.
- Traceability and Credibility: All results generated by LENSai can be referenced back to their original sources, ensuring authenticity and reliability in research outcomes.
"I am pleased to share that BioStrand's advanced AI platform has now integrated a Retrieval Augmented Generation plugin with a Large Language Model, marking a significant technological advancement. This integration, viewed through the prism of our patented HYFTs, not only simplifies complex information but opens a new dimension of predictive analysis. It notably enhances the platform's ability to delineate the structure, function, and potential applications of large molecules, representing a meaningful step towards more intuitive and insightful data analysis in the life sciences sector.
"As we focus on advancing drug discovery and development processes, we have commenced a limited release of our platform through a well-structured phased rollout strategy extending over the upcoming year. This approach is aimed at ensuring a smooth transition into IPA's existing customer offerings while also allowing us to collect valuable feedback. The feedback garnered will be instrumental for the ongoing refinement and optimization of the platform, ensuring it continues to meet the evolving needs of our clientele," shares Dirk Van Hyfte, MD, PhD, Co-Founder and Head of Innovation of BioStrand.
LENSai: The Next-Generation Advanced AI Platform
BioStrand has successfully rolled out a next-generation unified knowledge graph-LLM framework for holistic life sciences research. At the core of their LENSai platform is a comprehensive and continuously expanding knowledge graph that maps a remarkable 25 billion relationships across 660 million data objects, linking sequence, structure, function, and literature information from the entire biosphere. Their first-in-class technology provides a comprehensive understanding of the relationships between genes, proteins, structures, and biological pathways, thereby opening powerful new opportunities for drug discovery and development. The platform leverages the latest advances in ontology-driven NLP and AI-driven LLMs to connect and correlate syntax (multi-modal sequential and structural data) and semantics (functions). BioStrand’s unified approach to biomedical knowledge graphs, RAG models, and LLMs combines the reasoning capabilities of LLMs, the semantic proficiency of knowledge graphs, and the versatile information retrieval capabilities of RAG to streamline the integration, exploration, and analysis of biomedical data, potentially unlocking a realm of uncharted possibilities.
About 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 modeling, and complex artificial intelligence systems to support its proprietary technologies in bioplatform-based antibody discovery. 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.
Forward Looking Information
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. Forward-looking information contained in this news release includes, but is not limited to, statements relating to the expected outcome on the drug development process of the integration of IPA’s LENSaiin silico humanization platform with its HYFT technology, and statements relating to IPA’s expected increased revenue streams and financial growth. 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.
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. 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, 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. 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, 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). 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. 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, are subject to change after such date. The Company does not assume any obligation to update or revise any forward-looking statements, whether written or oral, that may be made from time to time by us or on our behalf, except as required by applicable law.
Source: ImmunoPrecise Antibodies Ltd.