Nabla Bio Used AI to Design Antibody Candidates
The firm utilized its JAM-2 model to create drug candidates capable of targeting cellular receptors.
Updated on Sept. 28, 2026 in Biotech
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Nabla Bio has generated successful antibody candidates using its proprietary JAM-2 AI model, which utilizes test-time scaling to refine biological outputs. The company plans to move these AI-designed molecules into first-in-human clinical trials within the next two years.
Why it matters
By automating the design of viable antibodies, Nabla Bio aims to accelerate the drug discovery pipeline for complex proteins. This capability could significantly shorten the time and cost required to develop therapies for difficult therapeutic targets.
The JAM-2 model achieved a 30 to 70 percent precision rate on user-defined epitopes, while over 50 percent of generated candidates met developability criteria without further optimization.
The players
Nabla Bio
A Massachusetts-based biotechnology company focused on using artificial intelligence to design and develop new antibody therapies.
Takeda
A global research-based pharmaceutical company that has expanded its partnership with Nabla Bio to leverage AI-driven drug design.
Chai Discovery
A biotechnology firm that raised $400 million in a recent funding round to further its own AI-based biological research.
The details
The JAM-2 model successfully generated antibody candidates against 26 distinct targets using iterative reasoning processes. These candidates demonstrated the ability to activate cellular signaling pathways, a critical requirement for functional therapeutic efficacy.
Timeline
Nabla Bio and Takeda announced a partnership expansion in October 2025.
Chai Discovery secured $400 million in funding during mid-2026.
The company expects to initiate first-in-human clinical trials between 2027 and 2028.
Deeper Dive
This development follows the structure of the Takeda-Nabla Bio partnership, which serves as a testing ground for integrating advanced AI models into traditional pharmaceutical pipelines.
While these developments occur in early-stage research, the successful use of AI in medicine could eventually reduce the time it takes for new life-saving treatments to reach patients. This shift may ultimately lower development costs, potentially influencing the long-term price of complex specialty medications.
The takeaway
AI models like JAM-2 are beginning to handle complex tasks like antibody design that were previously manual and time-consuming. These early successes suggest that computational biology will play an increasingly critical role in the future of clinical medicine.
Further reading
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Source note: This article includes information reported by Crypto Briefing.
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