The global AI drug discovery industry's financing record is about to be broken.

 

According to Bloomberg, Isomorphic Labs, an AI drug discovery company spun out of Google DeepMind and led by a Nobel laureate, is advancing a new financing round of over US$2 billion (approximately RMB 13.6 billion). The round is led by renowned investment firm Thrive Capital, with participation from Google's parent company, Alphabet.

 

If this financing round is successfully completed, Isomorphic Labs will set the record for the largest single financing round in the global AI drug discovery industry, representing an unprecedented capital bet in the global biopharmaceutical sector.

 

Combined with its US600 million Series A financing completed in March 2025, this four-year-old company will have raised a total of US600millionSeriesAfinancingcompletedinMarch2025,thisfour−year−oldcompanywillhaveraisedatotalofUS2.6 billion in less than one and a half years.

 

What makes this financing extraordinary goes far beyond the staggering amount. In the traditional biopharmaceutical industry, it would be almost unimaginable for a company with no pipeline in the clinic and no clinical data validation to attract billions of dollars in funding. This challenges the long-held beliefs of conventional pharmaceutical investors.

 

Traditional drug R&D has long followed the "10 years, US1 billion, 90% failure" adage – the average R&D cost for a new drug exceeds US1billion,901 billion, the cycle lasts over a decade, and 90% of drug candidates entering clinical trials ultimately fail.

 

The core reason capital is willing to bet on Isomorphic Labs is not short-term pipeline returns, but rather a wager that the company, backed by Google's computing power and Nobel Prize-level foundational technology, can use AI to completely restructure the entire drug R&D process, fulfilling founder Demis Hassabis's ultimate vision of "using AI to cure all diseases within a decade."

 

TONACEA 01: What Justifies a US$2 Billion Financing?

 

Isomorphic Labs' confidence has been backed by top-tier industry advantages since its inception. The company was spun out of Google DeepMind. Its founder, Demis Hassabis, is the 2024 Nobel Laureate in Chemistry. His team created AlphaFold2, which, with its ability to accurately predict protein structures, has become a landmark achievement in "AI + life sciences," fundamentally reshaping the foundational tool for global drug R&D.

 

Distinct from the traditional trial-and-error R&D model focused on single targets and single drugs, Isomorphic Labs' core logic is to build a generalizable AI drug design engine, breaking down R&D barriers across different diseases and targets, enabling universal reuse across the entire field.

 

From the perspective of technological iteration, the company's core capabilities continue to deliver: in 2024, in collaboration with DeepMind, it launched AlphaFold3, achieving accurate predictions of the structures and interactions of all life's molecules; in early 2026, it released the drug design large language model IsoDDE, with small molecule binding affinity prediction capabilities surpassing traditional physics-based methods. The accuracy of protein-ligand structure prediction has doubled compared to AlphaFold3, establishing it as one of the world's leading AI drug discovery models.

 

The intervention of AI technology can indeed significantly improve R&D challenges. According to market analysis firms, by 2028, AI will save over US$70 billion in drug discovery, and also halve the early-stage drug discovery timeline.

 

On the technology implementation front, Isomorphic has long been tied to global top-tier multinational pharmaceutical companies (MNCs), having signed major R&D collaborations with Novartis and Eli Lilly with a total scale exceeding US$3 billion. The company's president previously revealed that internal drug candidates are approaching the clinical stage. Although specific targets and therapies have not yet been announced, a key signal of technological implementation has been sent to the market.

 

With Nobel Prize-backed foundational algorithms on one side, and Google's continuous computing power support on the other, coupled with commercial collaboration guarantees from top pharmaceutical companies, capital is willing to pay for its technological potential even without clinical data. This US$2 billion financing is essentially capital's extreme premium on the long-term value of AI in reshaping pharmaceutical R&D – a bet on a paradigm shift that could upend the traditional pharmaceutical industry.

 

TONACEA 02: From Pipeline-Driven to Technology Platform Dominance

 

The massive financing of Isomorphic Labs is not an isolated incident; it is a microcosm of the ongoing dramatic shift in investment logic within the global biopharmaceutical industry.

 

For a long time, traditional pharmaceutical investment has adhered to the iron rule of pipeline value priority: whether a company's pipeline has entered the clinic, whether clinical data meet the standards, and whether the commercialization prospects are clear – these have been the core basis for capital decisions, with the technology platform serving merely as a supplement.

 

However, the entry of AI is gradually changing this logic. Foundational AI technology platforms are increasingly becoming the core lever for capital and pharmaceutical companies to structure their investments. Capital is no longer solely focused on a specific drug or a particular target, but is betting on generalizable technologies that could reshape the R&D efficiency of the entire industry.

 

This logic shift is being fully implemented in the strategic layouts of global MNCs. Top-tier MNCs are frenetically increasing their bets on AI drug discovery, with intensive and massive investments. An AI drug discovery arms race has already begun.

 

In 2024, Eli Lilly and Novartis both partnered with Isomorphic Labs, reaching collaborations exceeding US1.7 billion and US1.7billionandUS1.2 billion, respectively.

 

In January 2026, Eli Lilly successively partnered with AI drug large model company Chai Discovery and NVIDIA. The collaboration with Chai Discovery provides access to core algorithms capable of designing new antibodies from scratch, not limited to a single drug, with the aim of building a dedicated AI R&D system. The collaboration with NVIDIA involves a US$1 billion joint investment plan over 5 years to build an AI drug factory.

 

The most recent example comes from Merck & Co. On April 23, 2026, Merck announced a strategic collaboration with Google Cloud valued at up to US$1 billion, aiming to deploy an intelligent agent platform across Merck's business operations, transforming it into an AI-driven enterprise, leveraging Google's computing power and AI technology to empower its entire drug discovery and clinical development process.

 

As the global AI drug discovery arms race accelerates, a critical window for China to overtake is emerging.

 

According to incomplete statistics, as of early 2026, 113 AI drug discovery companies have been established in China. Global-level AI-native pharmaceutical companies have emerged, including XtalPi, BioMap, Metis Pharmaceuticals, and DeepZhiyao, achieving independent breakthroughs in foundational technologies such as protein prediction, molecular generation, and clinical large language models. Metis Pharmaceuticals is approaching a Hong Kong IPO. XtalPi has advanced multiple AI small molecule drug candidates to Phase II clinical trials. BioMap's immune large language model efficiently discovers new targets and antibodies. Chinese companies are taking a global lead in AI drug delivery and intelligent manufacturing processes. Local leaders such as Hengrui Medicine, BeiGene, and CSPC Pharmaceutical Group are accelerating their deployment of computing power and algorithms, deeply integrating with domestic AI platforms to build self-controlled new drug R&D systems.

 

China's AI drug discovery is moving from technological catch-up to platform output, seizing a critical position in the global R&D paradigm shift.

 

Furthermore, financing and collaborations in the global AI drug discovery field continue to accelerate, extending from early-stage small molecule discovery and protein prediction to antibody drugs, cell and gene therapies, clinical trial design, and the entire drug production chain.

 

According to a report released by Precedence Research, the global AI drug discovery market size reached US6.93 billion in 2025, and is expected to climb to US6.93billionin2025,andisexpectedtoclimbtoUS17.81 billion by 2035, with a compound annual growth rate of approximately 10%, highlighting the significant favor capital holds for this field.

 

Currently, more than 800 well-known pharmaceutical companies worldwide have advanced innovative collaborations in AI drug discovery, including Fortune Global 500 giants such as Johnson & Johnson and AstraZeneca. Domestic traditional pharmaceutical companies such as Hengrui Medicine and CSPC Pharmaceutical Group are also actively positioning themselves. The focus of pharmaceutical companies' strategic deployment is shifting from "buying pipelines and buying drugs" to buying algorithms, buying computing power, and buying foundational R&D platforms. Even some AI-native pharma companies without clinical pipelines can secure major collaborations with top-tier pharmaceutical companies and attract capital favor based on their core technologies.

 

A new global drug revolution, led by AI, is rapidly approaching.