AI Drug Discovery Track Is Getting Increasingly Lively.

 

On June 30, local time, at "The Briefing: AI for Science" event held in San Francisco, Anthropic — valued at approximately $965 billion and approaching its IPO — officially launched Claude Science, an AI work platform for researchers, and announced the initiation of an internal drug discovery program.

 

From Google DeepMind's continued investment in life sciences, to reports that ByteDance is spinning off its AI drug discovery team to pursue independent financing, and now to Anthropic's announcement of its internal drug R&D efforts, major AI model companies are accelerating their entry into innovative drug R&D — a field traditionally dominated by biotech and pharmaceutical companies.

 

AI drug discovery is also entering a new phase of development.

 

01、From AI Assistant to Drug Discovery First-Hand

 

Claude Science, launched by Anthropic, is an AI research platform designed for scientists. It can be used for literature analysis, biological data processing, experimental design, and research code generation, aiming to help researchers improve scientific efficiency.

 

Unlike previous offerings that primarily provided general AI capabilities, Claude Science is specifically integrated for life science scenarios, combining Claude models with research tools, specialized databases, and research workflows to help researchers complete tasks more efficiently. Anthropic stated that the platform will first be available as a beta version to enterprise paying users, with plans to continue expanding its application capabilities in the life sciences field.

 

However, more than Claude Science itself, what drew greater industry attention was Anthropic's first public announcement of an internal drug discovery program.

 

According to Eric Kauderer-Abrams, Head of Life Sciences at Anthropic, the internal drug discovery program will focus on neglected diseases — areas where traditional biopharmaceutical companies have long underinvested due to limited commercial returns. He stated that Anthropic launched this program not merely to explore drug discovery itself, but more importantly, to bring AI directly into the front lines of scientific research. "To build the right models, products, and tools to accelerate the industry, we have to be part of this process firsthand — there is no substitute for the experience gained by working alongside everyone in drug discovery."

 

This statement also reveals Anthropic's approach to AI drug discovery: rather than simply providing model services to pharmaceutical companies, the company aims to establish a tighter feedback loop through real scientific research scenarios, continuously optimizing models, products, and tools to accelerate AI capability iteration in life sciences.

 

In other words, Anthropic's internal drug discovery program is not a pivot to becoming a pharmaceutical company, but rather a means to gain deeper insight into the actual needs of research and pharmaceutical processes through firsthand participation — thereby refining AI products better suited for the life science industry and further deepening collaborations with pharmaceutical companies and research institutions.

 

This also signals a shift in Anthropic's role within the pharmaceutical value chain. Previously, it primarily served as a technology provider behind the scenes, offering models, algorithms, and computing power to support R&D processes. Now, it is moving toward deeper involvement in drug discovery, evolving from providing general AI tools to becoming a research and development partner in the life sciences.

 

This also marks a shift in the path of AI commercialization. As general-purpose large models mature, more AI companies are seeking new value-driven application scenarios. Life sciences — characterized by long R&D cycles and data- and knowledge-intensive processes — are considered one of the most promising directions.

 

02、Anthropic's Life Sciences Landscape

 

Looking at the longer timeline, the launch of Claude Science and the internal drug discovery program are not a one-off decision, but rather part of Anthropic's continued strategic push into life sciences.

 

Since 2025, Anthropic has systematically built out its life sciences business, and Claude Science is the company's first vertically oriented product specifically for research institutions, pharmaceutical companies, and the life science industry.

 

On the partnership front, Anthropic has collaborated with multiple multinational pharmaceutical companies. Public information shows that Novo Nordisk has applied Claude to drug discovery, clinical documentation, regulatory submissions, and scientific literature organization; AstraZeneca has used Claude to improve R&D efficiency and scale its R&D operations.

In May this year, Bristol Myers Squibb (BMS) also announced it would deploy Claude Enterprise widely across the company, covering R&D, drug development, manufacturing, and commercial operations — further expanding AI applications throughout the pharmaceutical value chain.

 

Beyond partnerships, Anthropic has also strengthened its life sciences capabilities through acquisitions. In April this year, the company acquired biotechnology startup Coefficient Bio, founded by computational biology and machine learning researchers from Genentech, which focuses on using AI to improve drug discovery and biology research efficiency. Industry observers widely believe the acquisition is more about strengthening life sciences talent and foundational technology capabilities than acquiring mature products.

 

Another detail worth noting is the guest lineup at Anthropic's event. In addition to Anthropic CEO Dario Amodei, the event featured BMS Chairman and CEO Chris Boerner, Novartis CEO and Anthropic board member Vas Narasimhan, and Genentech R&D head Aviv Regev — all key figures in life sciences — joining the discussions.

 

For a product launch by an AI company, such a guest lineup is uncommon and reflects the growing eagerness of global pharmaceutical giants to collaborate with AI companies in exploring life science applications.

 

From product launches and pharma partnerships to talent acquisitions and internal drug discovery, Anthropic is steadily building out its life sciences business ecosystem.

 

03、AI Drug Discovery Enters the "Era of Large Models"

 

Anthropic is not alone in betting on life sciences and diving deep into AI drug discovery.

 

Over the past few years, AI drug discovery has been primarily driven by AI biotechs such as Insilico Medicine, Recursion Pharmaceuticals, and Xaira Therapeutics — focusing on target discovery, molecular design, and clinical development, while attracting significant capital investment.

 

Now, new players are entering the arena. Overseas, Anthropic has announced internal drug R&D; in China, earlier this year, ByteDance announced it would spin off its AI drug discovery team from the Seed division into an independent company and pursue external financing to further advance AI drug R&D and commercialization.

 

This means the competitive landscape of AI drug discovery is expanding from specialized AI biotechs to include large model companies with foundational models, computing power, and capital advantages.

 

A key question is: why are more large model companies turning their attention to life sciences?

 

On one hand, as large model technologies mature, AI companies need to find application scenarios with greater commercial value. Compared to general applications like office productivity, customer service, and programming, innovative drug R&D not only has a massive market but also features high technological barriers and high added value. If AI can truly participate in this space, the value it creates will far exceed that of merely providing model services.

 

On the other hand, life sciences represent one of the fields where large models can best demonstrate their capabilities — from massive literature reading and multi-omics data analysis, to target discovery, molecular design, and experimental protocol optimization. Drug development spans nearly all of AI's strengths in information processing, reasoning, and generation. For large model companies, this is not only a new application scenario but also an opportunity to move from "exporting model capabilities" toward "creating intellectual property" and "building R&D assets" — opening up new commercial possibilities.

 

For large model companies preparing for IPOs or facing ongoing commercialization pressure, this extension of the value chain holds particular significance. In the past, AI company revenues primarily came from model API calls and enterprise services. In the future, if they can participate in innovative drug R&D — and even own proprietary drug pipelines and patent assets — their business models and valuation logic in capital markets could shift accordingly.

 

Of course, this does not mean AI can independently develop new drugs. After drug discovery, the process still requires experimental validation, animal testing, clinical trials, and other lengthy steps. Whether AI can truly translate into market-approved products remains to be seen.

 

But a trend is becoming increasingly clear: AI companies are no longer content to be mere "assistants" to pharmaceutical companies. They want to be participants in innovative drug R&D — and even redefine the competitive rules of the life sciences industry.

 

From selling models to developing new drugs; from providing AI capabilities to creating intellectual property — this may well be the next frontier in large model commercialization.

 

— Summary —

 

The internet changed the flow of information; mobile internet changed how we connect; and AI is now attempting to change scientific research itself.

 

As more large model companies step into laboratories and participate in drug discovery, scientific research, and life science studies, they bring not only new competitors but also the potential to drive the entire innovative drug R&D ecosystem toward greater efficiency.

 

In the future, what AI truly changes may not be any single pharmaceutical company — but the speed at which new drugs are born, and the way humanity confronts disease.