Nuclera, the biotechnology company enabling rapid access to high-quality proteins, today announced the launch of its antibody screening service to streamline the transition from antibody hit generation to lead selection. The service will support researchers seeking to identify the most promising antibody candidates to progress to costly mammalian expression and functional testing, addressing a key bottleneck in AI-driven antibody discovery. The launch follows the Company’s Series C extension, enabling the integration of antibody expression and binding validation to support antibody discovery workflows.
Nuclera’s antibody service delivers a rapid upstream triage workflow that identifies viable binders early on in the process between in silico hit generation and mammalian scale-up. By utilizing 96-plex binary cell-free expression and binding assays, the service screens full length antibody libraries in parallel, rapidly narrowing large candidate sets to a focused subset of confirmed binders. Surface Plasmon Resonance is then performed on prioritized hits for binding kinetics.
Despite advances in bioinformatics and in silico antibody design, a major bottleneck remains in experimentally validating large numbers of candidates. Traditional antibody secondary screening methods are often slow and fragmented, which typically result in large amounts of resources being spent on non-binders through costly processes.
As AI-driven approaches generate increasingly large antibody libraries, the need for rapid, cost-effective triage solutions has become more critical. Nuclera’s antibody service addresses this gap by converting large AI-generated libraries into experimental binding data and enabling early elimination of non-binders, reserving expensive downstream biology for candidates proven to bind and accelerating progression to validated leads.
Dr Michael Chen, CEO and co-founder, Nuclera, said: “Antibodies are one of the most important classes of therapeutic molecules, yet antibody discovery remains inefficient, with many initially promising candidates failing during downstream validation. A key bottleneck is the cost of recombinant antibody expression and binding validation, which limits the generation of high-quality data, and is holding back the full potential of AI/ML discovery. The launch of our antibody service addresses this challenge by enabling rapid triage of large candidate sets and delivering decision-grade binding data early in the discovery process at a competitive cost. By helping teams focus on the most promising candidates before scale-up, we are taking an important step toward enabling more effective use of AI in antibody discovery.”
