- Imperagen, based in Manchester, closed a £5 million seed round led by PXN Ventures
- The company combines quantum simulation, custom AI models, and robotic lab automation into a closed-loop system that improves with every experiment
- Imperagen improved enzyme performance by more than 500 times in just five development cycles
Imperagen, a UK-based deeptech, raised £5 million in seed funding led by PXN Ventures, with ongoing support from IQ Capital and Northern Gritstone.
The Manchester spin-out, founded in 2021 by Dr Andrew Almond, Dr Andrew Currin, and Dr Tim Eyes, is building a closet that uses quantum custom AI models and lab automation to design enzymes faster and more predictably than traditional methods.
Engineering enzymes for specific uses is still slow, expensive, and unreliable, the startup claims. Its platform features a continuous three-stage process. First, quantum physics simulations model millions of possible enzyme mutations, generating a large dataset of predicted properties.
Automated robots run these lab experiments, and the results go straight back into the AI model to improve its predictions for the next round. With each cycle, the process becomes more focused and effective. For example, when working with a Fortune 500 personal care company, Imperagen’s platform improved the performance of two target enzymes by 677- and 572-fold in just five cycles.
On the competition side, there are protein engineering companies like Absci and Arzeda, as well as AI-driven drug discovery firms moving into industrial biotech, such as Exazyme and Charm Industrial. Imperagen stands out by using quantum-level simulation as its primary means of generating data.
Most competitors use existing experimental data or general protein language models to train their AI, but Imperagen creates its own high-quality training data before running any physical experiments. As biological foundation models improve, the company will need to see if this approach continues to give it an edge.
The fresh investment will fund core R&D, expand lab capacity, grow the AI team, and boost efforts to turn commercial interest into revenue in pharmaceuticals, life sciences, personal care, sustainable fine chemicals, and industrial biotech.
