Biocytogen, a global biotechnology company focused on advancing antibody-based drug discovery through innovative technologies, has launched RenSuper Workstation, a next-generation AI-powered antibody discovery platform, along with the RenSuper High-Throughput Antibody Manufacturing Automation Center. The newly introduced platforms are designed to accelerate therapeutic antibody discovery, validation and scalable manufacturing through an integrated, intelligence-driven ecosystem.

Built on Biocytogen’s proprietary RenMice platforms, RenSuper combines in vivo immune repertoires, AI-based candidate selection, automated experimental validation and high-throughput manufacturing capabilities into a closed-loop antibody discovery engine. The platform supports the development of monoclonal antibodies, bispecifics, multispecifics, Antibody-Drug Conjugates (ADCs), VHHs and other advanced therapeutic modalities, significantly reducing the time and complexity involved in early-stage biologics research.

RenSuper Workstation enables researchers to access a large-scale library of experimentally validated, fully human therapeutic antibody sequences against more than 1,000 validated targets. The platform introduces a programmable and searchable approach to antibody discovery, allowing rapid identification and comparison of high-quality therapeutic candidates based on target specificity, epitope selection and physicochemical characteristics.

According to the company, RenSuper’s AI models are trained on more than 100 million biologically validated antibody sequences, enabling efficient screening and candidate selection even for structurally complex and biologically challenging targets. Using Biocytogen’s RenNano fully human HCAb platform as an example, the AI-powered workflow achieved an average positive hit rate of 46 percent, with peak hit rates reaching 98 percent. The workflow also delivered an 82 percent success rate for high-purity Fc-format antibody expression.

All AI-selected antibody candidates undergo experimental validation, including binding affinity and specificity assessments, ensuring reproducibility and development readiness for downstream applications. The integrated platform also supports rapid antibody sequence assembly and flexible therapeutic format design.

Complementing the discovery platform, the newly launched RenSuper High-Throughput Antibody Manufacturing Automation Center introduces fully automated protein production and validation workflows. The system covers bacterial inoculation, plasmid extraction, transfection, fed-batch cultivation and purification processes with minimal manual intervention.

The automation infrastructure is capable of producing antibody yields averaging between 50 mg and 100 mg, while supporting throughput of up to 800 samples per day and more than 5,000 samples per week. The company stated that the facility is designed to transform antibody validation from a labour-intensive process into a scalable industrial workflow.

Commenting on the launch, Dr. Yuelei Shen, Founder and CEO of Biocytogen, said, “We believe the future of antibody discovery will be driven by speed, scale and data-driven intelligence. With RenSuper, we are building an intelligence-native system that makes antibody discovery more intelligent, scalable and efficient. Our goal is to reduce the time and complexity of early-stage discovery, accelerate the development of innovative therapeutics and bring new medicines to patients faster.”

Biocytogen stated that the combined launch of RenSuper Workstation and the automated manufacturing centre strengthens its long-term vision of transforming therapeutic antibody discovery into a more intelligent, scalable and programmable process for global biologics innovation.

Founded on gene-editing technology, Biocytogen has developed a dual-engine platform integrating fully human antibody libraries with an extensive portfolio of target-humanised mouse models. The company focuses on accelerating global drug discovery and development through advanced biotechnology solutions and collaborative research platforms.