Enzymes Move to the Center of AI-First Biotech Manufacturing
Enzymes, the biological catalysts that drive life’s chemistry, are rapidly becoming core infrastructure for modern biotech manufacturing. They enable cleaner, more efficient processes in pharmaceutical manufacturing, personal care, sustainable chemical production, and wider industrial biotech. By lowering energy usage, reducing waste and cutting production costs, engineered enzymes are an essential tool for sustainable chemical production and next-generation drug development acceleration. Yet designing an enzyme that works under real-world industrial conditions has traditionally been slow, manual and expensive, with low hit rates from conventional screening. That bottleneck has limited how fast companies can bring greener chemistries and novel drug pathways to market. AI enzyme engineering promises to change this equation by compressing design–build–test cycles and systematically exploring sequence space in ways that are impossible for human researchers alone, opening the door to more ambitious applications in both therapeutics and industrial biocatalysis.
Inside Imperagen’s Closed-Loop AI Enzyme Engineering Platform
Imperagen represents a new class of AI-native “techbio” companies built around closed-loop, data-driven experimentation. Its proprietary platform links quantum physics simulations, problem-specific AI models and automated lab robotics into a single recursive system. First, quantum physics models generate a vast landscape of possible enzyme mutations and predict key properties in silico. These simulated datasets are then used to train tailored AI models that focus narrowly on the engineering problem at hand, rather than relying on generic algorithms. The most promising variants are handed off to automated robotics in the wet lab, which test performance under relevant conditions. Crucially, the resulting high-quality experimental data flows straight back into the AI models. Each iteration refines the search space, continuously improving prediction accuracy. This closed-loop architecture allows Imperagen to progressively converge on high-performing enzymes in far fewer rounds than traditional methods, exemplifying how AI enzyme engineering can scale beyond human trial-and-error.
From Years to Iterations: Compressing Enzyme Timelines for Pharma and Chemicals
Traditional enzyme engineering can take years, with researchers manually screening large libraries and often missing optimal variants. Imperagen’s closed-loop AI platform shows how drug development acceleration and sustainable chemical production can benefit from radically shorter cycles. In one demonstration, its system improved the productivity of two different enzymes by 677-fold and 572-fold, respectively, in just five rounds of optimisation. Each loop through simulation, AI-driven design and robotic testing narrows focus onto increasingly effective variants, replacing brute-force exploration with intelligent, data-guided searches. For pharmaceutical partners, this means faster access to biocatalysts that enable new synthetic routes, more efficient active ingredient manufacturing, or gentler processing conditions for sensitive molecules. In industrial biotech and fine chemicals, the same capabilities can unlock novel reactions, convert fossil-based steps to bio-based processes, and de-risk scale-up. The result is a tighter integration of computational design with automated experimentation, shrinking the gap between concept and viable enzyme.
Funding Momentum Signals Confidence in AI-First Biotech Platforms
Imperagen’s £5 million seed round, led by PXN Ventures with participation from existing backers IQ Capital and Northern Gritstone, highlights growing investor confidence in AI-powered biotech manufacturing. Capital is being directed not just to single products, but to platforms that can repeatedly generate optimised enzymes across pharmaceuticals, life sciences, personal care, sustainable fine chemicals and industrial biotech. The company plans to use the funding to accelerate its core R&D platform, expand wet lab capacity and scale its in-house AI team, including both human and agentic capabilities. It will also invest in go-to-market efforts to convert rising commercial interest into revenue, building on work with clients such as a Fortune 500 personal care company. The appointment of experienced executive Guy Levy-Yurista as CEO underscores ambitions to turn Imperagen’s deep tech stack into a scalable business, positioning closed-loop AI enzyme engineering as a foundational layer for future bioprocesses.