Recently, DeepModeling completed a Series C funding round totaling over 800 million RMB. The investment was jointly provided by institutions including Genesis Capital, Jingguo Rui Fund, Beijing Artificial Intelligence Industry Investment Fund, Beijing Pharmaceutical and Health Industry Investment Fund, Lenovo Capital, and Yuanhe Puhua.

The funds raised will primarily be used to continuously attract and cultivate top-tier talent within the industry, further evolve and iterate DeepModeling’s “Scientific Discovery Intelligent Engine,” and solidify its full-stack capabilities ranging from original technological innovation to intelligent scientific research tools and industry solutions. This will accelerate the market expansion and large-scale application of AI-driven scientific discovery products and services in fields such as basic scientific research, life sciences, and physical sciences.

The completion of this financing marks a significant step forward for DeepModeling in its journey to build a next-generation intelligent engine for scientific discovery.

AI for Science Becomes Global Consensus; Paradigm of Scientific Discovery is Being Reconstructed

We stand at a historic juncture where “AI for Science” has become a global consensus. Its goal is to fundamentally transform the way humans explore the unknown, discover new scientific knowledge, and systematically deposit it as reusable scientific assets.

In August 2025, the State Council issued opinions on deeply implementing the “Artificial Intelligence+” initiative. The document placed “AI + Scientific Research” at the forefront, specifically emphasizing the acceleration of the scientific discovery process to drive innovation in technological R&D models and enhance efficiency.

Furthermore, Europe’s Horizon program has prioritized AI-empowered scientific research. The United States has launched the Genesis Mission, which focuses on accelerating scientific breakthroughs through AI, improving the efficiency of AI-driven scientific discovery and industrial application conversion. This mission has been elevated to a strategic level comparable to the Manhattan Project.

Tech giants such as Google DeepMind, NVIDIA, and Microsoft continue to invest heavily in this area, while primary market investors are frequently backing innovative companies within the sector.

This global initiative, aimed at enhancing humanity’s fundamental innovation capabilities and fully mining scientific potential, has officially entered a new era of exploration.

Behind this global consensus, four core tasks for AI for Science have gradually become clear.

Scientific research is the process by which scientists use various tools to explore the world. In the past, scientific productivity was severely limited due to outdated and inefficient traditional tools, as well as the small number of intellectual minds directly participating in research. Moreover, human intelligence was difficult to effectively amplify and accumulate.

Now, against the backdrop of AI deeply empowering the intelligent transformation of traditional research tools and AI agents creating new participants in scientific discovery, AI for Science is bringing systemic reconstruction opportunities to the framework of scientific discovery:

AI activates scientific data, AI reshapes scientific software, AI drives scientific experiments, and AI creates scientists.

From Tools to Infrastructure: How DeepModeling Builds “AI Scientists”

As a pioneer and leader in global AI for Science, DeepModeling relies on years of deep cultivation in the field and a profound understanding of the industry. It has built an ability system centered around “Bohr Research Space Station,” encompassing “Read, Compute, Do, and Intelligence.” This has created a Science as a Service (SaaS) intelligent scientific research product and service matrix, including:

• Bohr Science Navigator
• Bohr Lebesgue Intelligent Computing and Hermite®, Piloteye® series of micro-scale R&D software
• Bohr Cyber Lab
• SciMaster Scientific Agent
• “Large-Scale Facilities” for scientific discovery and R&D services

These offerings aim to provide deep and broad, flexible combination solutions for scientists and R&D organizations in the fields of basic research, life sciences, and physical sciences.

To date, DeepModeling’s Bohr Science Navigator has served over 3 million scientists from more than 1,000 universities and organizations globally. Nearly 100 prestigious institutions, including Peking University, Shanghai Jiao Tong University, and Wuhan University, have fully onboarded to the platform. It has supported thousands of scientific research projects, answered approximately 12 million scientific questions annually, and saved scientists over 2 billion minutes of working time.

Additionally, DeepModeling’s scientific intelligence products and solutions have assisted in the intelligent upgrade of R&D systems for more than 150 advanced R&D enterprises. It has deeply empowered more than 70 life science companies, including Fosun Pharma, Sinopharm, Hansoh Pharmaceutical, Huadong Medicine, Dongyue Group, Qilu Pharmaceutical, Unilever, and Yunnan Baiyao, across more than 100 R&D pipelines. The company also serves physical science clients such as Suzhou Laboratory, CNPC, China Iron & Steel Research Institute Group, CATL, BYD, and GAC Aion, helping partners create over 50 high-value scientific assets.

According to calculations by partners, the intelligent upgrade of the R&D system is a highly cost-effective investment. It can improve the efficiency of literature review and organization by a hundredfold. The introduction of AI computing methods has reduced wet lab requirements and costs by 76%, while the popularization of smart labs has increased the usage efficiency of experimental instruments and throughput by more than three times. “AI Scientists” can reduce scientists’ trivial and repetitive workloads by approximately 70%.

The underlying infrastructure supporting this rich product service system and research ecosystem is DeepModeling’s “DeepModeling · Yuzhi®” foundation, which has been continuously built over seven years.

Initially a pre-trained large model system for the scientific field, “DeepModeling · Yuzhi®” has now been comprehensively upgraded into an Intelligent Engine for Scientific Discovery:

Driven by scientific agents, it connects the closed loop of “Read—Compute—Do,” constructing the shortest path to humanity’s unknown knowledge for AI4S scenarios. “Read” integrates existing knowledge, “Compute” explores and generates unknown spaces, and “Do” completes the verification loop. Thus, the “AI Scientist” becomes a discovery entity capable of learning, thinking, executing, and providing feedback.

Centered around this intelligent engine for scientific discovery, DeepModeling unifies and deposits scientific data, computing, and experimental capabilities into callable R&D infrastructure:

• Bohr Science Navigator has integrated knowledge content from over 170 million high-quality English literature articles, more than 200 million patents, and 80 million Chinese literature articles;
• Vertical application models built on AI4S large models for directions such as atoms, molecules, genes, and proteins have exceeded one thousand;
• Relying on Bohr Cyber Lab (the operating system), over 100+ frequently used experimental instruments have been integrated;
• Through automated compilation and deployment capabilities, it supports the unified invocation of more than 50,000 scientific tools in an Agent-Ready format;
• The platform is currently serving more than 3 million scientist users from over 1,000+ universities and research institutions globally. Continuous usage and feedback in real scientific tasks are gradually forming an ecological cycle where “research tools—research content—research personnel” mutually promote each other.

Relying on the capabilities and ecosystem accumulated through the “DeepModeling · Yuzhi®” Intelligent Engine for Scientific Discovery, DeepModeling is driving “AI Scientists” from single-point applications toward an intelligent system of scientific production:

By collaborating with ecological partners, it abstracts research processes and methodologies from different disciplines into combinable agent modules. This allows scientific agents to be rapidly constructed, continuously evolved, and constantly giving rise to new “Master Agents.”

As a representative achievement, ML-Master has achieved leading performance in top-tier evaluations such as HLE and MLE. MatMaster, jointly released with Suzhou Laboratory, demonstrates the system’s implementation and extension capabilities in specific disciplines.

The resulting efficiency advantage stems from the long-term accumulation of closed-loop infrastructure and real-world usage feedback, forming a core barrier that is difficult to replace with single-point models or isolated tools.

△ The DeepModeling · Yuzhi® large model system has been comprehensively upgraded into an Intelligent Engine for Scientific Discovery

Logic Behind Betting on a Long-Term Track: Why Now, and Why It Can Sustain

Just as search engines constructed the shortest path to humanity’s known information, and large language models further built the shortest path to humanity’s known knowledge, AI4S is constructing the shortest path to humanity’s unknown knowledge.

DeepModeling’s mission and vision are to build “AI Scientists” that help humans discover new scientific achievements, along with a series of intelligent systems capable of autonomous scientific discovery, making scientific discovery as simple as using a search engine.

Based on this new paradigm, empowering the intelligent upgrade of global R&D systems will liberate scientists from complex and repetitive labor, allowing them to focus on creative inspiration. This will systematically accelerate the process of human scientific discovery and application implementation, generating stronger innovation efficiency for the nearly $2.8 trillion annual investment in scientific research and R&D, equivalent to almost 100 million full-time jobs worldwide.

Zhang Linfeng, Founder and Chief Scientist of DeepModeling, stated:

In DeepModeling’s view, AI for Science is not just an emerging track but a foundational infrastructure project for scientific discovery over the next few decades. From the DeepModeling · Yuzhi® large model system to research platforms and solutions tailored for different domain scenarios, and further to domain-specific “AI Scientists” like PharmMaster and MatMaster, DeepModeling has always maintained long-term investments centered on “real scientific problems” and “real industrial needs.”

What DeepModeling aims to achieve is a self-consistent Intelligent Engine for Scientific Discovery, ensuring that AI serves not merely as an accelerator for specific links.

Through this round of financing, DeepModeling will focus on enhancing the usability and evolutionary capacity of this engine in real-world research scenarios. On one hand, it will promote the continuous emergence of frontier scientific capabilities; on the other, it will accelerate its implementation and application in life sciences, physical sciences, and other fields, fostering the continuous emergence of “AI Scientists” across various domains.

Sun Weijie, Founder and CEO of DeepModeling, stated:

This round of financing occurs at a critical stage where the state is deeply implementing the “Artificial Intelligence+” initiative and AI for Science has been elevated to the forefront of global technological competition. It is not only an acknowledgment of DeepModeling’s phased progress but also a trust in our mission for the next phase.

In the future, DeepModeling will continue to uphold the philosophy of “accelerating scientific discovery and releasing scientific value.” With the long-term goal of creating “AI Scientists” and “Intelligent Systems for Scientific Discovery,” we will further accelerate the value creation of AI for Science in basic research and industrial R&D. We strive to grow into a tech company originating from China but leading globally, allowing next-generation intelligent scientific infrastructure to exert greater international influence.

Notably, some institutional representatives participating in this financing round also highly praised AI for Science and DeepModeling.

Wang Dakui, Managing Director of Genesis Capital, stated:

DeepModeling is a scarce, hardcore target in the “AI for Science” track and a platform-level enterprise defining research paradigms. The company possesses deep core barriers and has already crossed the critical step from technology to market. Its constructed scientific intelligence discovery engine has served hundreds of clients, validating its commercialization path.

As a platform company, it faces the challenge of simultaneously advancing deep penetration and large-scale replication in long-cycle industries such as pharmaceuticals and materials. Fortunately, the company’s top-tier “Academician-Scientist-CEO” team provides valuable trial-and-error space and ecological synergy for the enterprise within complex industries. The team’s long-term strategic vision and efficient execution have further strengthened our confidence in its future development.

The Beijing Artificial Intelligence Industry Investment Fund and the Beijing Pharmaceutical and Health Industry Investment Fund stated:

As long-term observers and investors of DeepModeling, we firmly believe in its profound accumulation and outstanding breakthroughs in the field of AI for Science.

DeepModeling’s full-chain empowerment capabilities allow us to see a clear picture of AI-driven basic research moving from laboratories to industrialization. We believe that DeepModeling is not only an enterprise focused on technological innovation but also a key leader driving global scientific research into an “Intelligent Acceleration Era.”

Rooted in Beijing, DeepModeling deeply leverages the city’s abundant scientific resources, rapidly transforming frontier theories from laboratories into industrializable technology platforms. It has become a typical sample of the closed loop of “industry-academia-research-application.” We are confident in its long-term value and will continue to accompany the company’s growth, jointly witnessing the great process of AI reshaping scientific discovery.

Zhu Ping, Managing Director of Jingguo Rui Fund, stated:

As an investor in DeepModeling, we have long held a positive outlook and firmly supported its development. As a leader in the new paradigm of “AI for Science” research, its innovative scientific intelligence discovery engine is generating

fundamental industries such as biomedicine and energy materials.

The deep integration of AI and scientific research is a global trend. We believe that DeepModeling, with its top-tier interdisciplinary team and solid technical foundation, is well-positioned to play a key role in the new round of technological revolution. We are delighted to continue accompanying and supporting the company’s growth, jointly promoting the industrialization of cutting-edge technologies, and contributing to the national innovation strategy.

Wang Guangxi, Vice President of Lenovo Group and Managing Partner at Lenovo Capital, stated:

AI for Science is ushering in a “Age of Discovery” for scientific research. This not only represents a reconstruction of the scientific research paradigm but also presents a strategic opportunity to enhance national innovation capabilities.

As a pioneer in this field, DeepModeling has built a full-stack capability ranging from underlying models and intelligent tools to industrial applications through its “Scientific Discovery Intelligent Engine” and the “Bohr” product matrix. These have been widely validated in both academic and industrial circles, demonstrating significant potential in driving a revolution in research efficiency and empowering industrial upgrades.

Lenovo Capital has long focused on AI-driven industrial intelligence transformation. Leveraging Lenovo Group’s global supply chain and industrial ecosystem advantages, we will continue to support DeepModeling in accelerating the improvement and application expansion of intelligent scientific infrastructure, jointly pushing China’s AI for Science to the forefront globally.

Bohr Research Station:
https://www.bohrium.com SciMaster:
https://scimaster.bohrium.com/ Hermite®:
https://hermite.dp.tech Piloteye®:
https://www.bohrium.com/org/piloteye PharmMaster:
https://pharmmaster.bohrium.com/ MatMaster:
https://matmaster.bohrium.com