Algorithms Are Now Europe’s Hottest Biotech Talent

By 2025, AI is speeding up mRNA design and redrawing biotech’s competitive lines across Europe

4 Feb 2026

Artificial intelligence is increasingly shaping how mRNA medicines are designed, and by 2025 it is beginning to influence the competitive balance within Europe’s biotech sector. Tasks that once required months of laboratory iteration are now being guided by algorithms that predict which genetic designs are most likely to work before experiments begin.

The use of AI in mRNA research has been developing for several years, but its application has moved beyond academic experimentation. European research groups have published models that optimise mRNA sequence design using relatively small datasets, focusing on areas such as codon choice, molecular stability and protein expression. These tools are now being applied in industry, affecting how companies organise discovery programmes and deploy research spending.

mRNA development involves a large number of design choices, from sequence structure to delivery performance. AI systems can search vast design spaces quickly and reduce them to a narrower set of candidates with higher chances of success. This allows research teams to focus laboratory work on the most promising options and move selected programmes towards clinical testing more efficiently.

BioNTech has been among the most vocal European groups on the role of advanced computing in mRNA development. Its acquisition of AI specialist InstaDeep in 2023 was intended to embed machine learning across research and discovery, rather than using it as a separate analytical tool.

Analysts say the main benefit of AI is not only speed but greater confidence at early stages of development. Improved prediction reduces uncertainty in an environment where funding is tighter and late-stage failure is expensive. However, adoption across Europe remains uneven. Large, well-capitalised companies are integrating AI into core workflows, while smaller biotechs often depend on partnerships, academic spinouts or external platforms.

This divide is shaping collaboration and deal-making. Some companies are building proprietary systems, while others selectively apply cloud-based tools to specific projects. Digital capability is becoming an increasingly important point of differentiation.

Regulatory scrutiny, demands for transparency and questions over data ownership remain unresolved. Even so, AI is becoming a standard layer in mRNA research. As tools become more trusted, Europe’s biotech competition is likely to be shaped as much by algorithms guiding early decisions as by laboratory results that follow.