Imperagen Raises $6.6M Seed for AI and Quantum Enzyme Engineering

Jan de Vries · 2026-05-21

Imperagen, a Manchester-based BioTech company, is using AI and quantum physics to engineer better enzymes faster. They just closed $6.6 million (5 million pounds) in Seed funding to accelerate R&D, expand their wet lab capabilities, and build out their go-to-market function over the next 18 months. ### The Funding Round and New Leadership The round was led by PXN Ventures, with participation from existing investors IQ Capital and Northern Gritstone. This brings Imperagen's total funding to $11.3 million (8.5 million pounds). Along with the cash, Guy Levy-Yurista, PhD, joins as the new CEO. He's a seasoned technology and life sciences executive with two successful exits across the US and Europe. Dr. Levy-Yurista shared his excitement: "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."  ### What Makes Imperagen Different? Imperagen is a spin-out from the University of Manchester, founded in November 2021 by Dr. Andrew Almond, Dr. Andrew Currin, and Dr. Tim Eyes. The company claims to deliver the most improved and production-ready enzyme designs by rapidly retraining problem-specific AI models with real-world data. Traditional enzyme engineering relies on manual screening, which is slow, costly, and has a low hit rate. Newer zero-shot techniques promise smart designs but often underperform in real-world settings. Neither approach gives industrial clients the predictability and speed they need. Imperagen's proprietary platform combines three stages into a single closed-loop system: - Quantum physics simulates millions of mutation combinations in silico, generating a dataset of predicted properties. - Problem-specific AI models are trained on those outputs, calibrated to the precise engineering challenge. - Automated robotics test the highest-performing predictions in the physical lab, producing high-quality experimental data that feeds back into the AI model. ### Real-World Results The feedback loop is what sets this approach apart. Each round of experiments makes the next round more targeted. The system learns from the wet lab as it goes, narrowing in on the highest-performing variants with each iteration. Imperagen serves diverse markets, from pharmaceutical manufacturing and life sciences to sustainable fine chemical production and industrial applications. They've already worked on significant projects, including with a Fortune 500 personal care company. In just five rounds, their AI-guided system improved enzyme productivity by 677x and 572x for two different enzymes. ### What's Next? Sim Singh-Landa, Investment Director at PXN, noted: "The North West's life sciences ecosystem is becoming stronger all the time and stands to gain from Imperagen's local hiring and growth plans. We're excited to support Imperagen as they scale success in enzyme engineering." With fresh funding and new leadership, Imperagen is poised to make a real difference in how we engineer enzymes for everything from sustainable chemicals to life-saving medicines. It's a great example of how deep tech can solve real-world problems.