Chinese AI pharmaceuticals are competing for a more central position in global innovation.

On October 19th, Pharmaron Technology announced a strategic partnership with global pharmaceutical giant Eli Lilly. Both parties will conduct research and discovery on novel lipid nanoparticle (LNP) delivery solutions, exploring tissue-specific delivery technologies applicable to multiple treatment modes.

According to publicly available trading channels, this is the first strategic cooperation in China driven by a generative AI platform and centered around LNP delivery. It is also the largest AI driven extrahepatic LNP delivery platform cooperation in the world in terms of transaction scale. According to the terms of the agreement, Zhitai Technology will receive a down payment of over ten million US dollars and is eligible to receive development, regulatory, and commercial milestone payments totaling up to 1.1 billion US dollars. In addition, Zhitai Technology can also receive graded royalty fees based on the single digit percentage of annual net sales of cooperative products.

The significance of this cooperation lies not only in the partnership between Yitai and global pharmaceutical giants, but also in the technological direction and industry development trends that the cooperation points towards.

Since 2026, AI is accelerating its penetration into the entire process of drug research and development, and the competition among Chinese AI pharmaceutical companies is shifting from efficiency improvement to breakthroughs in core technologies. From designing drug molecules to exploring how to accurately deliver drugs to target tissues and cells, Chinese companies are trying to move from being efficiency promoters of global research and development to participants in the next generation of technological routes.

Delivery technology is one of the key incisions. For therapeutic payloads such as nucleic acids and proteins, designing potential molecules is only the first step; Whether it can effectively reach the target tissue, enter the target cells, and release the payload also affects whether its therapeutic potential can be realized.

Generative AI has the opportunity to extend its research and development capabilities from molecular design to the design and optimization of delivery systems. Delivery is no longer just a supporting link in drug development, but may also become a platform field with independent technological value.

01. Yitai takes over Lilly BD, Biological AI welcomes industrial validation

Since the beginning of this year, the "friend circle" of Jitai Technology has become increasingly lively.

From top investment platforms to several leading pharmaceutical companies in China, now the list of collaborations with Yitai Technology has welcomed another heavyweight player - Eli Lilly.

As a global pharmaceutical giant, Lilly's "move" carries a guiding significance. Unlike a single established molecular authorization transaction, this time, Lilly has turned its attention to the AI delivery platform of Zhitai. For AI technology that is deepening its development towards biomedicine, this is undoubtedly a signal worth paying attention to: Biological AI is moving from a technical concept to more specific industrial cooperation.

Dr. Lai Caida, Chairman and CEO of Yetai Technology, stated in the mid-term performance release in August 2026 that the NanoForge platform is transitioning from a single drug delivery design to a comprehensive and systematic design platform covering proteins, nucleic acids, delivery systems, and cell states, marking the company's entry into a new stage of Biological AI in the era of "programmed life".

The NanoForge platform independently developed by Yitai Technology is currently the only end-to-end AI nanodelivery infrastructure in the world. It is not a single 'delivery toolbox', but a Biological AI operating system covering the entire chain of 'biological instructions - nanorockets - precise release'.

Specifically, AiRNA and AiProtein are responsible for designing mRNA and protein (biological instructions), AiLNP is responsible for designing delivery vectors (nanorockets), and cell targeting technologies such as tLNP achieve precise release - the collaborative design and joint optimization of the three essentially involves "reading, writing, and modifying" cellular programs.

Lilly has chosen to engage in a strategic partnership with Yetai around AiLNP, focusing on the ability of the NanoForge platform to continuously design, explore, and optimize delivery systems using AI.

After all, designing a promising molecule with AI is just the first step in the development of new drugs. Next, we still need to solve a practical problem - how to get it where it should go?

Especially for drugs such as nucleic acids, the delivery system is not a simple combination after molecular design. Even if the molecule itself has potential, it is difficult to fully exert its function if it cannot effectively enter the target cell. Whether delivery can break through directly affects whether the value of molecular design can be truly realized.
The core logic of Biological AI also lies in this. AI not only assists scientists in discovering molecules, but can also further participate in the design of biomolecules, the development of delivery vehicles, and cell targeting, driving research and development from "what to design" to "how to deliver".

In this sense, this collaboration allows Biological AI to no longer just be a technological narrative about future research and development models, but to further enter the industrial cooperation scene of large global pharmaceutical companies. For Zhitai, the value of NanoForge is no longer solely reflected in the platform's technological layout, but is beginning to be validated through specific industry collaborations.  

02. Why is it necessary to add the delivery link to Lilly's AI map?

Currently, AI is becoming a battleground for pharmaceutical giants. From the discovery of new drugs to production and manufacturing, MNCs are constantly increasing their efforts to compete for the initiative in next-generation pharmaceutical technology.

Eli Lilly is one of the active planners. Since 2026, its AI footprint has continued to expand: partnering with Nvidia to build LillyPod and AI Co Innovation Lab, laying out computing infrastructure; Explore the application of AI in the design of small molecule, biopharmaceutical, and gene editing tools through collaborations with Insilicon Intelligence, Chai Discovery, and Profluent; Acquiring Kelonia Therapeutics further expands the layout of in vivo gene delivery. In addition, Lilly is also promoting the application of technologies such as digital twins, AI quality inspection, and robots in the manufacturing process.

These layouts may seem scattered, but they actually point to different links in the pharmaceutical industry chain, where AI not only helps researchers discover and design new therapeutic molecules, but also participates in production and manufacturing, enabling research and development results to be more effectively applied. And between molecular design and therapeutic efficacy, there is another technical problem that must be solved, which is how to reach the target cells and exert its effects after drug design?

The cooperation with Zhitai Technology responds to the aforementioned challenges.

Jitai Technology is laying out LNP delivery technology with a generative AI platform. Under its NanoForge platform, AiLNP focuses on utilizing AI to generate and optimize lipid nanoparticles, exploring delivery strategies in different loading, tissue, and cellular scenarios. Unlike directly designing drug molecules, this technological approach focuses on how drugs are encapsulated, distributed within the body, and enter target cells to release the payload.

If AI molecular design answers' what medicine to make ', then AI delivery needs to answer' how to deliver medicine to where it should go '.

These two issues are closely related. The design of drug molecules determines their potential therapeutic effects, while the physicochemical properties, in vivo stability, tissue distribution, and release kinetics of the delivery system also affect whether the payload can reach the target site, enter the target cells, and exert its effect. For nucleic acid drugs and some protein therapies, delivery capacity is particularly critical.

Therefore, the cooperation between Yitai and Eli Lilly this time is not just an AI pharmaceutical company winning another multinational pharmaceutical company BD. What is more noteworthy is that Eli Lilly's AI layout is further extending from molecular discovery and manufacturing to delivery technology; And what Jitai Technology provides is a platform capability for generative design and continuous optimization around LNP delivery.  

03. From chemical screening to AI generation, the value of LNP is soaring

Traditional LNP development heavily relies on the experience of chemists and high-throughput experimental screening. Researchers design lipid structures, prepare formulations, and then test delivery effects through experiments, repeatedly adjusting parameters to find better solutions.

This method has already supported the development of LNP technology, but as the research and development goals extend from relatively mature delivery scenarios such as the liver to the lungs, spleen, tumor tissues, and specific immune cells, the difficulty of research and development is also increasing.

The competition for delivery is shifting from "whether it can be delivered in" to "whether it can be accurately delivered to the target cells".

The chemical structure, stability, and release characteristics of LNP will all affect the final delivery performance. To find formulas suitable for different tissues, cells, and payloads in the vast chemical space, relying solely on empirical design and screening one by one is facing increasing challenges.

Generative AI provides another approach: not only can it search for better solutions in existing formulas, but it can also generate new lipid structures from scratch, and then combine model predictions and experimental feedback for continuous iteration.

The NanoForge platform of Yitai Technology has accumulated over 10 million lipid structures and 100000 experimental data points. Its AiLNP platform has a de novo lipid generation algorithm (AI for Nano), a lipid language based model, and the ability to conduct dry wet experiments in a closed-loop manner.

The value of these accumulations goes far beyond establishing a massive lipid structure library. Generative AI can further propose new lipid structures, and continuously explore potential delivery solutions through predictive models and experimental feedback.

It can be said that a large part of the reason why Eli Lilly chose Yitai Technology is its ability to continue discovering new formulas and the value that this ability may create in the future.

This is also a noteworthy aspect of this collaboration, as the value of AI pharmaceuticals is extending from improving molecular discovery efficiency to redesigning drug delivery systems. For Jitai, the cooperation with Eli Lilly is not only a commercial development, but also provides new validation opportunities for its generative AI delivery platform to move towards industrial applications.

Of course, from AI generated candidate lipids to demonstrating their delivery effectiveness in the human body, further experimental research and clinical development are still needed for validation. But the signal released by this cooperation is already clear: delivery is no longer just a supporting link in drug development, but is also becoming an independent field where AI platforms can participate in design, optimization, and value creation.

If the platform of Zhitai can continuously discover new lipid structures, optimize delivery performance in different scenarios, and ultimately be validated in humans, then the company's commercial value may open up another imaginative space: it may not only depend on a candidate drug or a set of existing formulas, but also on the platform's ability to continuously develop new delivery solutions.  

- Freehand summary -

Behind this cooperation is an upgrade in the competitive logic of China's AI pharmaceutical industry.

In the past, Chinese AI pharmaceutical companies participated in global innovation, and cost advantages and research and development efficiency were important chips. Nowadays, with the extension of generative AI to core technology fields such as drug delivery, Chinese companies have the opportunity to enter the core of global innovation through independent technology platforms.

The services provided by Zhitai are not simply CRO style R&D, but rather a delivery platform driven by generative AI, jointly exploring new lipid structures and delivery solutions with multinational pharmaceutical companies. This means that the role of Chinese AI pharmaceutical companies is shifting from being executors in the R&D chain to being joint explorers of key technology routes.

The dimension of competition has changed from 'Chinese companies making AI pharmaceuticals cheaper' to 'Chinese AI platforms participating in defining next-generation delivery technologies'.

Chinese AI pharmaceuticals are not only competing for research and development efficiency and market share, but also for their position and voice in the evolution of next-generation biopharmaceutical technology.