WORK Medical Technology Group has entered into a strategic cooperation agreement with Shanghai Novabioplus Biotechnology to develop next-generation intelligent medical models using AI and biological data.
The collaboration addresses a growing need in biopharmaceutical R&D: unlocking the value of large biological datasets and applying them more effectively to drug discovery, protein engineering, and translational research. As life sciences companies generate increasingly complex molecular and laboratory data, AI-driven platforms are becoming more relevant in accelerating design, simulation, and optimisation workflows.
Under the agreement, WORK Medical will combine its AI algorithm capabilities with Novabioplus’ protein datasets and biological reagent platforms. The partnership will focus on four “AI+” project areas: membrane protein design, antibody sequence optimisation, enzyme molecule design, and peptide design.
A key differentiator of the collaboration is its proposed “BioToken” assetisation model. By integrating protein sequences, functional data, and laboratory datasets into an intelligent digital protein database, the partners aim to transform biopharmaceutical R&D outputs into verifiable and measurable digital assets.
The initiative also aligns with WORK Medical’s broader “Healthcare + Web3 + AI” strategy, extending its ambitions beyond medical device manufacturing into life sciences data infrastructure. For Novabioplus, the partnership provides an avenue to apply its protein science, cell science, and quality research platforms to AI-enabled biomedical innovation.
The target users and beneficiaries include biopharmaceutical companies, research institutions, and development teams working in protein therapeutics, antibody engineering, enzyme design, and peptide-based applications. If successfully developed, the model could support more efficient biomolecular design and improve how biological data is used across early-stage R&D.
Industry observers note that the success of the collaboration will depend on the maturity and reliability of the AI models, the ability to validate biological design outputs, and the establishment of robust data governance frameworks. Acceptance of tokenised biological assets also remains uncertain, as the “BioToken” concept is still in its early stages and has yet to demonstrate broad commercial or scientific utility beyond its digital asset framework.
In the coming months, attention will likely focus on the development of the four AI+ research areas, as well as the validation of the intelligent digital protein database that underpins the platform. Demonstrating measurable gains in biomolecular design efficiency and R&D productivity will be critical for the partnership as it seeks to position itself within the evolving intersection of AI, biotechnology, and digital infrastructure. The collaboration reflects a wider trend across the life sciences industry, where companies are increasingly exploring how AI and large-scale biological datasets can be leveraged to create new forms of scientific and commercial value.
