AI-driven antibody discovery could create a $7 billion market opportunity for life science tools as drug developers increase the scale of laboratory testing.

Artificial intelligence could drive demand for laboratory tools used in antibody discovery rather than reduce the need for experimental research, according to an analysis from Leerink Partners.

The investment bank estimates that AI-enabled antibody discovery could represent a total addressable market of around $7 billion for life science tools, with a $4 billion opportunity linked to biologics drug discovery.

The analysis describes an emerging approach known as Lab-in-the-Loop (LitL), in which artificial intelligence models generate potential antibody sequences that are then tested experimentally. Data from those experiments can subsequently be fed back into the models to inform the design of the next generation of candidates.

The approach could accelerate antibody discovery by creating a continuous cycle between computational design and laboratory testing.

According to the Leerink analysis, the increased use of AI could also create greater demand for technologies capable of experimentally validating large numbers of candidates.

This could be particularly relevant to binding affinity measurements, which assess how strongly an antibody binds to its target. The analysis identifies surface plasmon resonance (SPR) among the technologies used to generate this data and names Carterra as one of the suppliers operating in the space.

Carterra, which develops high-throughput SPR platforms, highlighted the report as evidence of growing demand for technologies supporting AI-driven biologics discovery.

Josh Eckman, CEO and co-founder of Carterra, said: “This report reinforces what we’re hearing directly from our customers: as AI generates larger and more complex sets of antibody candidates, the bottleneck shifts to how quickly and accurately those candidates can be validated at the bench.”

The company said its platforms are designed to support high-throughput analysis of antibody candidates, potentially helping researchers generate experimental data at the scale required by AI-driven discovery programmes.

The report also highlights the role of companies including Twist Bioscience and Adaptyv Bio in the emerging Lab-in-the-Loop ecosystem.

Twist Bioscience has previously described the use of Carterra’s technology to generate antibody binding data in its work with AWS Biodiscovery, while Adaptyv Bio CEO and co-founder Julian Englert said demand for high-throughput protein engineering technologies was increasing.

The broader shift towards Lab-in-the-Loop drug discovery reflects the growing use of AI to design and prioritise potential therapeutic molecules.

However, AI models still depend on experimental data to determine whether computationally designed candidates have the desired biological properties. In antibody discovery, this can include measuring binding affinity and other characteristics that cannot be reliably established through computational modelling alone.

This means the growth of AI-driven drug discovery could shift bottlenecks within research workflows rather than eliminate the need for laboratory experimentation.

Leerink’s analysis estimates that around $1.3 billion of the $4 billion biologics drug discovery opportunity is currently outsourced to service providers, highlighting the potential commercial impact of increased demand for experimental validation.

The report’s estimates represent projected market opportunities rather than established market revenues, and the extent to which AI-driven discovery changes demand for specific laboratory technologies remains to be seen.

For companies developing high-throughput analytical platforms, however, the growing integration of AI and laboratory experimentation could create new opportunities as drug discovery programmes generate increasing numbers of candidates requiring experimental testing.