Chai Discovery and argenx have partnered to apply generative AI for de novo antibody discovery, accelerating immunology research and next-generation biologic development.
Written By: Chikkula Pavan Kumar, PharmD
Reviewed By: Pharmacally Editorial Team
Chai Discovery has entered a collaboration agreement with argenx to integrate artificial intelligence into antibody drug discovery. Under the agreement, argenx will gain early access to Chai’s AI platform to generate novel antibodies against therapeutic targets, expanding its immunology research capabilities.
The collaboration combines argenx’s expertise in immunology and antibody engineering with Chai Discovery’s generative AI platform, which predicts and engineers molecular interactions. Both companies aim to accelerate the identification of differentiated antibody candidates while reducing the reliance on traditional screening and optimization approaches.
The agreement reflects the growing adoption of AI-based molecular design across the biopharmaceutical industry as companies increasingly integrate computational models into early-stage drug discovery.
AI Platform Expands De Novo Antibody Design Capabilities
Antibody discovery has traditionally relied on screening large libraries followed by iterative optimization to improve binding affinity, specificity, and developability. Recent advances in generative AI have introduced a complementary strategy that computationally creates entirely new antibody sequences with predefined functional properties.
Chai Discovery develops AI models that predict interactions between proteins and other biomolecules, enabling researchers to generate antibody candidates before laboratory testing. The platform supports therapeutic binding optimization, multispecific antibody design, difficult-to-drug targets, and developability assessment within a single computational workflow.
The technology is intended to improve the efficiency of lead discovery by producing candidates that require fewer rounds of experimental refinement.
Chai-3 Builds on Earlier AI Model Performance
The collaboration provides argenx with access to Chai’s latest molecular design platform, Chai-3. The company’s earlier model, Chai-2, introduced in 2025, became the first zero-shot antibody design platform to report double-digit experimental hit rates, representing an approximately 100-fold improvement over previous computational antibody design methods. According to Chai Discovery, Chai-3 further improves prediction and design performance across therapeutic binding, multispecific antibody engineering, hard-to-drug targets, developability, and generalization to new molecular challenges.
Although no specific therapeutic targets, disease indications, clinical programs, or financial terms were disclosed, the collaboration focuses on applying these AI capabilities during the earliest stages of antibody discovery.
Leadership Highlights Scientific Value of the Collaboration
Joshua Meier, co-founder and Chief Executive Officer of Chai Discovery, said argenx combines scientific rigor with the ability to rapidly integrate emerging technologies into research workflows. He noted that providing access to Chai’s platform could help accelerate de novo antibody design for priority therapeutic targets.
Peter Ulrichts, PhD, Chief Scientific Officer of argenx, said the company’s innovation model integrates disease biology, antibody engineering, and rigorous experimental validation. He added that AI-driven molecular design represents a valuable extension of this discovery strategy.
Path Forward
The collaboration strengthens a broader industry trend toward integrating generative AI into biologics research, where computational molecular design is increasingly complementing laboratory-based discovery methods. If successful, the partnership could accelerate the identification of novel antibody candidates for future immunology programs and support more efficient progression from computational design to preclinical development.
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About the Writer
Chikkula Pavan Kumar (LinkedIn), PharmD is a Doctor of Pharmacy with a keen interest in clinical pharmacy, pharmacovigilance, and evidence-based practice. In his words, he is passionate about patient safety and translating complex medical information into clear, research-driven communication.