How Imperagen Is Using AI, Quantum Physics, and Automated Labs to Reinvent Enzyme Engineering

However, designing enzymes with the exact performance needed for industrial processes can be difficult. Traditional approaches frequently involve trial‑and‑error mutation and screening, where scientists create thousands of variants and test them in the lab to see which works best.

This process can take months or years and consume large amounts of laboratory resources.

Imperagen’s “Digital Enzyme Evolution” platform

Imperagen’s approach aims to digitize and accelerate the entire enzyme‑engineering pipeline. The company describes its platform as Digital Enzyme Evolution, integrating AI‑driven protein design, physics‑based modelling, and robotic laboratory automation into a unified workflow.

Instead of relying primarily on experimental screening, the platform runs a closed design‑build‑test‑learn cycle.

1. Quantum‑physics simulations

The first step uses physics‑based modelling to predict how enzymes behave at the molecular level.

Quantum‑mechanics simulations can model enzyme structure, reaction chemistry, and dynamic behavior before any laboratory experiments occur. This helps identify promising mutation strategies and reduce the number of variants that need to be tested physically.

2. Custom AI models for mutation prediction

Imperagen then uses machine‑learning models trained on experimental data to determine which changes to an enzyme’s sequence are most likely to improve performance.

These models help identify "hotspots" in enzyme structures—locations where targeted mutations may increase catalytic activity, stability, selectivity, or manufacturability.

Because the AI learns from data generated in each round of experiments, its predictions can improve over time.

3. Automated closed‑loop experimentation

Once the models propose new enzyme designs, automated laboratory systems build and test them.

Robotics perform the synthesis, expression, and testing of enzyme variants, and the resulting performance data feeds directly back into the computational models. This creates a continuous closed‑loop optimization cycle that can rapidly iterate toward improved enzymes.

Why this matters for multiple industries

Improved enzyme engineering has broad economic and environmental implications because enzymes act as biocatalysts in many industrial processes.

Pharmaceuticals and drug manufacturing

In pharmaceutical production, optimized enzymes can enable cleaner and more selective synthesis of complex molecules, potentially simplifying manufacturing steps and reducing chemical waste.

Imperagen’s technology was initially developed with applications in large‑molecule drug discovery and biocatalyst development.

Industrial biotechnology and sustainable chemicals

Enzymes are also increasingly used to replace traditional chemical catalysts in industrial processes.

Better enzymes could reduce energy consumption, raw‑material use, and harmful by‑products in sectors such as fine chemicals, personal care ingredients, and sustainable manufacturing.

Food, biofuels, and agriculture

The same design principles could apply to other sectors that depend on specialized enzymes—for example:

• Food processing and ingredient manufacturing
• Biofuel production using plant biomass
• Agricultural biotechnology

In these areas, improved enzymes may allow processes to run at higher temperatures, tolerate harsher industrial conditions, or convert lower‑cost feedstocks. Available sources describe these markets as potential targets but provide limited detail on Imperagen’s current deployments.

The role of the £5 million seed round

Imperagen recently secured £5 million in seed funding, led by PXN Ventures with participation from existing investors IQ Capital and Northern Gritstone.

According to company announcements, the capital will support:

• Expansion of its physics‑based simulation and AI modelling capabilities
• Further development of automated laboratory infrastructure
• Scaling the platform for commercial partnerships and customer projects

The funding announcement also coincided with the appointment of Guy Levy‑Yurista, PhD, as CEO, bringing an experienced technology and life‑sciences executive to lead the company’s next phase of growth.

The bigger picture for techbio

Imperagen represents a growing category of “techbio” companies that combine advanced computation with automated biology labs.

The core idea is simple but powerful: integrate simulation, AI prediction, and robotics into a single feedback loop that learns from every experiment. If successful, this approach could turn enzyme engineering from a largely experimental craft into a data‑driven engineering discipline.

That vision is still early. Much of the available evidence comes from company announcements, and independent benchmarks demonstrating exact improvements in speed, cost, or success rates remain limited. But if platforms like Imperagen’s prove scalable, they could significantly accelerate the development of enzyme‑based products across pharmaceuticals, chemicals, and industrial biotechnology.