LabGenius, a pioneer in the use of machine learning (ML) for antibody engineering, announced that it has been awarded a highly competitive SMART grant from Innovate UK. Grants from the £25 million fund allow businesses like LabGenius to realize the potential of new ideas by investing in game-changing, commercially viable research and development projects.
“We’re incredibly proud to have been awarded a SMART grant from Innovate UK”
To date, LabGenius’ EVA platform has been used to co-optimize mono- and multi-specific single domain antibodies for biochemical and bio functional properties, including stability, potency and selective tumor cell killing. This grant will be used to further expand EVA’s capabilities by accelerating the platform’s ability to optimize antibody-based immune cell engager molecules.
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Many existing antibody therapeutics have poor selective cell killing profiles, which can result in on-target, off-tumor effects, and the discontinuation of treatment. Using EVA’s predictive power, LabGenius is identifying non-intuitive protein designs, which perform well across multiple therapeutically valuable properties, including selective killing.
Commenting on LabGenius’ funding allocation, UKRI Board Member, Lord David Willetts said: “Machine learning presents real promise to transform the way we approach cancer detection and treatment. It is incredibly exciting to see LabGenius focusing its efforts in this area of therapeutic innovation by continuing to advance their immune cell engager lead optimization capability.”
“We’re incredibly proud to have been awarded a SMART grant from Innovate UK” says LabGenius’ CEO and Founder, Dr. James Field. “This funding will help us realize the potential of computational technologies in R&D as we believe that they are the key to accelerating the discovery and development of advanced therapeutics.”
Speaking to the company’s recent success, LabGenius’ Chief Scientific Officer, Dr. Gino Van Heeke said, “Lead optimization is a critical part of the antibody discovery process, and this funding will allow us to further demonstrate our platform’s potential to use machine learning to accelerate the development of molecules with best-in-class properties.”