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.
漢康生技與陽明交大啟動AI賦能藥物研發合作 推進次世代創新生物藥開發
結合生成式AI與實驗驗證，優化候選分子設計與篩選流程以強化研發能力
漢康生技（TPEx：7827）為一家致力於開發次世代免疫療法、聚焦腫瘤與自體免疫疾病的全球臨床階段生技公司，今日宣布與國立陽明交通大學工程生物科學學院簽署人工智慧（AI）藥物共同開發合作備忘錄。雙方將結合陽明交大在AI模型建置、序列預測與電腦分析方面的專長，以及漢康在生物藥工程與轉譯驗證方面的能力，推進AI賦能生物藥研發，提升早期候選分子設計與評估的效率。
隨著生成式AI技術快速發展，人工智慧在生物藥物研發中的角色，已逐步由資料分析延伸至前端分子設計。漢康生技此次與國立陽明交通大學工程生物科學學院、由曾任院長之楊進木教授領導之跨領域研究團隊展開合作。此次合作初期將聚焦運用AI驅動模型，針對選定標的預測並生成抗體序列，並透過後續實驗測試評估其結合能力、功能表現及開發潛力。漢康認為，此一方法有助於提升候選分子篩選效率、降低前期大規模篩選負擔，並強化創新生物藥研發所需的分子設計能力。
相較於仰賴廣泛候選庫建立與大規模篩選的傳統研發流程，雙方認為，AI賦能方法有機會在研發早期即縮小候選範圍，並在特定情境下縮短前端發現流程的部分作業時程。
此次合作除聚焦候選抗體的生成與驗證外，也將朝向建立「AI設計—實驗驗證—數據回饋—模型再優化」的閉環開發模式。未來實驗所得數據可持續回饋至模型端，用於優化後續抗體序列生成能力。若此模式運作順利，雙方合作可望逐步發展為具持續迭代能力的AI藥物開發平台，進一步提升面對不同抗原標的時的研發效率與成功率。
漢康生技研發長翟文武博士表示，此次合作反映漢康持續建構差異化研發能力的整體策略，目標在於提升創新生物藥研發的品質、效率與可擴展性。透過結合AI賦能設計與實驗驗證，我們期望建立更有效率且具調適能力的研發框架，以支持次世代生物藥開發。
陽明交大楊進木教授表示，本次合作所採用的AI技術，係以本實驗室建立之篩選模型為基礎，目標在於透過資料驅動建模，於研發早期優先篩選出較具潛力的候選分子，同時維持對其分子特性與功能活性的嚴謹實驗驗證。
透過結合生成式AI與實驗驗證，漢康生技與陽明交大的合作可望提升前期藥物設計與篩選流程的效率，並逐步建立可持續優化的AI賦能研發能力，以支持未來更多創新生物藥開發專案。
關於陽明交大工程生物科學學院 藥物設計與系統生物實驗室
工程生物科學學院藥物設計與系統生物實驗室，由曾任院長之楊進木教授帶領，聚焦運用人工智慧、巨量資料、物理化學原理與系統生物學方法，解析藥物、蛋白質、生化途徑、細胞行為與疾病機制之間的關聯。實驗室透過電腦輔助藥物設計與系統生物研究，致力連結計算研究與轉譯醫學，並與生物及臨床研究團隊密切合作，以實驗與臨床數據驗證模型表現。
關於漢康生技
漢康生技（股票代碼：7827.TPEx）是一家全球臨床階段的生物技術公司，專注於腫瘤免疫學及自體免疫疾病領域，研發總部設於台北，並在上海及美國舊金山灣區設有運營辦公室。公司由一支在生物藥發現與全球開發方面擁有豐富成功經驗的資深團隊領導，致力於重塑癌症治療格局。漢康生技專有的Fc基礎設計生物藥平台能夠開發具有多種靶向模式的多功能生物藥，旨在激活先天性與適應性免疫通路，以突破當前抗PD-1/L1免疫療法的局限。該平台已在多個體內腫瘤動物模型中成功獲得概念驗證數據。通過差異化的分子研發策略與可規模化的CMC工藝開發，漢康生技正推進一系列創新生物藥管線，致力於解決尚未被滿足的重大醫療需求。