First Batch of Exemplary AI Use Cases in Biomanufacturing Announced, Showcasing New Paradigms for Technological Breakthroughs and Industrial Transformation

Amid deepening integration between the digital and bioeconomies, the Ministry of Industry and Information Technology (MIIT) recently announced the first batch of exemplary artificial intelligence (AI) use cases in biomanufacturing. These cases demonstrate new paradigms for technological breakthroughs and industrial transformation, covering areas from biological breeding and fermentation engineering to biosynthesis and intelligent detection. They reveal how AI technologies—through data-driven approaches, algorithm optimization, and intelligent decision-making—are addressing long-standing challenges in traditional biomanufacturing, such as prolonged cycles, low efficiency, and high costs, thereby accelerating the industry’s transition toward precision, flexibility, and sustainability.

The complexity of biomanufacturing stems from its dual nature as both a "biological system" and an "engineering system." The integration of AI is reshaping the core logic of this system. Wu Xu, Senior Manager of the Solutions Business Unit at Beijing DeepModeling Technology Co., Ltd., told Securities Daily that AI algorithms, by analyzing massive datasets and building predictive models, can shorten the R&D cycle in traditional pharmaceutical development from several years to just a few months, moving away from the conventional "trial-and-error" approach.

Among the exemplary use cases, Shanghai Tianwu Technology Co., Ltd. submitted a case focused on the design and construction of high-performance protein components. The company has developed a general-purpose AI model for proteins trained on 9 billion protein data entries. By integrating small-sample learning algorithms and a dry-wet lab iterative approach, the model enables end-to-end prediction "from sequence to function," establishing a new paradigm of "AI design supplemented by minimal experimental validation."

"The penetration of AI is triggering a chain reaction in the biomanufacturing ecosystem, giving rise to new value-creation models," said An Guangyong, an expert at the Credit Management Committee of the All-China Mergers & Acquisitions Association. He explained that AI platforms integrate gene editing, metabolic engineering, and process simulation technologies, shifting upstream R&D from "experience-driven" to "data-driven" and freeing researchers from repetitive experiments to focus on innovative strategy design. In midstream production, flexible manufacturing systems combined with AI scheduling algorithms enable a single production line to rapidly switch between different products. Downstream, AI-driven predictive maintenance and supply chain optimization dynamically adjust production capacity based on demand forecasting algorithms, building a new "Manufacturing as a Service" (MaaS) ecosystem.

An Guangyong added, “The integration of AI and biomanufacturing is not merely a technological overlay but a revolution in industrial paradigms. From the intelligent transformation of 'cell factories' to the green manufacturing of bio-based materials, and further to personalized precision medicine—AI is reshaping the underlying logic of the bioeconomy."

The announced use cases also include contributions from listed pharmaceutical companies, such as Beijing Tri-Times Technology Co., Ltd., which presented a case on intelligent control of bioprocesses in bioreactors.

Zhang Cuixia, Chief Investment Advisor at Jufeng Investment, told Securities Daily that in today’s rapidly evolving technological landscape, the deep integration of AI and biomanufacturing is becoming a core driver of transformation in the pharmaceutical industry. This integration is not only reshaping the fundamental logic of pharmaceutical R&D and production but also exerting a profound and multi-dimensional impact on listed pharmaceutical companies in terms of corporate capabilities, R&D models, and capital flows. For instance, AI-driven flexible manufacturing systems enable listed pharmaceutical companies to swiftly adapt their product lines to market demands. Meanwhile, the traditional pipeline quantity-based valuation model in capital markets is gradually being replaced by new standards centered around AI technological barriers, data assets, and intelligent production capabilities.

Zhang Cuixia emphasized that the fusion of AI and biomanufacturing is shifting the competitive focus of listed pharmaceutical companies from "scale competition" to "intelligent empowerment." This transformation requires companies to deeply integrate AI and biomanufacturing technologies and build open innovation ecosystems. Such changes are not only redefining corporate core competitiveness but also restructuring the logic of value assessment in capital markets.