HanchorBio and NYCU Launch AI-Enabled Drug Discovery Collaboration to Advance Next-Generation Biologics
Partnership combines generative AI and experimental validation to streamline candidate design and selection, and strengthen scalable discovery capabilities
[TAIPEI and SAN FRANCISCO, April 29, 2026] — HanchorBio Inc. (TPEx: 7827), a global clinical-stage biotechnology company developing next-generation immunotherapies for oncology and autoimmune diseases, today announced a research collaboration with the College of Engineering Bioscience at National Yang Ming Chiao Tung University (NYCU) to advance AI-enabled biologics discovery.
The collaboration is designed to integrate generative AI with experimental validation to improve the speed and efficiency of early-stage candidate design and evaluation. HanchorBio will combine NYCU’s expertise in AI model development, sequence prediction, and computational analysis with the Company’s biologics engineering and translational validation capabilities.
The initial focus of the collaboration is the use of AI-driven models to predict and generate antibody sequences against selected targets, followed by experimental testing to assess binding, function, and development potential. HanchorBio believes this approach may improve candidate selection efficiency, reduce the burden of early screening, and strengthen its broader molecular design capabilities for innovative biologics.
Compared with conventional discovery workflows that rely on broad library construction and large-scale screening, the collaborators believe this AI-enabled approach may narrow candidate space earlier and shorten parts of the front-end discovery process in selected settings. Over time, the collaboration is intended to establish a closed-loop development framework in which AI design, experimental validation, and data feedback continuously improve model performance and support future programs across multiple targets.
“This collaboration reflects our broader strategy to build differentiated discovery capabilities that can improve the quality, speed, and scalability of biologics innovation,” said Wenwu Zhai, Ph.D., Chief Science Officer of HanchorBio. “By combining AI-enabled design with experimental validation, we aim to create a more efficient and adaptive discovery framework to support next-generation biologics.”
“The AI technology used in this collaboration is based on screening models developed by our Drug Design and Systems Biology Laboratory,” said Jinn-Moon Yang, Ph.D., Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University. “Our goal is to use data-driven modeling to prioritize higher-potential candidates earlier in the discovery process, while maintaining rigorous experimental validation of their properties and functional activity.”
About the Drug Design and Systems Biology Laboratory, College of Engineering Bioscience, National Yang Ming Chiao Tung University
Led by Professor Jinn-Moon Yang, former dean of the college, the Drug Design and Systems Biology Laboratory focuses on applying artificial intelligence, big data, physico-chemical principles, and systems biology to understand the relationships among drugs, proteins, biochemical pathways, cellular behavior, and disease mechanisms. The laboratory seeks to bridge computational research and translational medicine through computer-aided drug design and systems biology, working closely with biological and clinical research teams to validate its models through experimental and clinical data.
About HanchorBio
HanchorBio (TPEx: 7827) is a global clinical-stage biotechnology company focused on developing next-generation immunotherapies for oncology and autoimmune diseases. With operations in Taipei, Shanghai, and San Francisco, the Company is advancing a portfolio of differentiated biologics enabled by its proprietary FBDB™ (Fc-Based Designer Biologics) platform. HanchorBio’s pipeline is designed to address complex disease biology through rationally engineered molecules with the potential to activate both innate and adaptive immune pathways.