Carterra®, a leading provider of high-throughput surface plasmon resonance (HT-SPR) platforms for antibody and small molecule discovery, today highlighted new research from Leerink Partners identifying Carterra as a key enabling technology in the fast-growing market for AI-driven biologics drug discovery.

In a report published July 16, 2026, titled “Lab-in-the-Loop: AI Rewires Antibody Discovery, Unlocking a $7B LST Opportunity,” Leerink Partners analyst Puneet Souda and team estimate a total addressable market of roughly $7 billion in life science tools demand tied to AI-enabled antibody discovery — including a $4 billion biologics drug discovery layer, of which an estimated $1.3 billion is currently outsourced to service providers. The report describes “Lab-in-the-Loop,” or LitL, as the emerging paradigm in which AI models propose candidate antibody sequences, wet lab experiments test them, and the resulting data feed back to sharpen each subsequent round of design — turning what was once a slow, serial discovery process into a faster, guided one.

Critically, the Leerink report finds that this shift increases rather than diminishes the need for wet lab data — and singles out binding affinity measurement, the step where gold-standard SPR instruments quantify how tightly an antibody binds its target, as a segment that should remain core to the loop rather than merely riding higher project volume. Leerink’s competitive landscape analysis names Carterra, alongside other technologies, as a primary supplier of this binding affinity infrastructure.

“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,” said Josh Eckman, CEO and co-founder of Carterra. “Carterra’s high-throughput SPR platforms were built for exactly this kind of scale, and we believe we’re well positioned to benefit as Lab-in-the-Loop becomes the default way biologics programs are run.”

Industry Voices Point to Rising Demand

Carterra’s customers, of which ten are named in the report, are already describing the shift in scale that Leerink’s report anticipates.

Twist Bioscience, which Leerink’s report identifies as the largest structural beneficiary of the Lab-in-the-Loop shift, has likewise pointed to Carterra’s role in generating the antibody binding data sets that large-scale AI discovery programs depend on. At the Protein and Antibody Engineering Summit (PEGS Boston) in May 2026, Twist Biopharma Solutions CSO, Dr. Colby Souders, described the part Carterra’s platforms play in its partnership with AWS Biodiscovery to generate binding data at the scale required by modern AI-guided discovery campaigns.

Julian Englert, CEO and co-founder of Adaptyv Bio, a protein engineering company supporting AI-driven biologics programs—also named in the report—said “ The market opportunity may be understated. AI in drug discovery is here to stay and we believe Leerink’s estimate of a ~$4B antibody discovery market may be low. At Adaptyv, we are seeing massive increases in both orders for our protein synthesis but also the size of projects being requested. We have deployed the most sophisticated and high-throughput technologies, like Carterra platforms, to maintain our leadership in the space.”

Why Binding Data is the Bottleneck

As Leerink’s report describes it, AI models are only as good as the experimental data used to train and refine them. Every round of the Lab-in-the-Loop cycle depends on fast, accurate, high-volume binding affinity data to tell the model which candidates actually work — and that data can only be generated in the wet lab. Leerink’s analysis notes that legacy approaches to binding characterization, often limited to simple yes/no assays or low-throughput instruments, are increasingly giving way to platforms built to match the scale of candidate pools that AI models now generate.

Carterra’s platforms combine proprietary microfluidics with real-time HT-SPR, delivering up to 100 times the throughput of traditional label-free platforms while using a fraction of the sample. The company has spent more than two decades developing label-free biosensor technology, with early support from Pfizer, and its systems are used across the majority of the world’s largest pharmaceutical companies today for both biologics and small molecule characterization.

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