Manchester’s Imperagen Raises €5.7M Seed To Advance AI-Driven Enzyme Engineering

The funding will support R&D expansion, wet lab development, and growth of its commercial team over the next 18 months.

The round was led by PXN Ventures, with participation from existing investors IQ Capital and Northern Gritstone, bringing total funding to €9.8 million (£8.5 million). The company has also appointed Dr Guy Levy-Yurista as its new CEO.

Founded in November 2021 as a spin-out from the University of Manchester, Imperagen was established by Dr Andrew Almond, Dr Andrew Currin, and Dr Tim Eyes. The company uses AI and quantum-inspired modelling to design improved enzymes more efficiently and with fewer development cycles.

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Its platform combines computational simulation, AI model training, and automated lab testing in a closed-loop system, allowing continuous improvement based on real experimental data. This approach aims to overcome the limitations of traditional enzyme discovery methods, which are often slow, expensive, and inefficient.

Imperagen serves industries including pharmaceuticals, life sciences, personal care, and sustainable chemicals, and has already demonstrated significant enzyme performance improvements in industrial projects.

The new capital will be used to scale research capabilities, expand laboratory operations, and strengthen its AI and commercial teams as it moves toward wider market adoption.

Dr Levy-Yurista said, “What I see right now is that the companies that will make a radical difference in this emerging AI-driven future are all AI-native, lean on real-world data, have genuine impact, and are fundamentally deep tech. Imperagen has each of those characteristics, combining them with outstanding people, phenomenal technology and the undeniable swagger you only get from Manchester. It was a no-brainer to join the team and lead this next stage in its growth.”

About Imperagen

Imperagen is a biotech company using AI and advanced simulations to design better enzymes. Its closed-loop platform combines machine learning, computational modelling, and lab testing to speed up development, reduce costs, and improve efficiency compared to traditional enzyme engineering methods.