Isomorphic Labs, the AI drug discovery company founded by DeepMind co-founder Demis Hassabis, is raising more than $2 billion in new funding to accelerate its mission of using artificial intelligence to redesign how medicines are created. The funding round, led by Thrive Capital with backing from parent company Alphabet, signals growing confidence that AI can solve one of medicine's most expensive and time-consuming challenges: drug development.

This funding milestone arrives at a pivotal moment. In 2025 and 2026, Isomorphic Labs advanced its first-in-human clinical trials for drugs designed using AlphaFold, the breakthrough AI model that Hassabis and colleague John Jumper won the 2024 Nobel Prize in Chemistry for creating. These aren't theoretical exercises anymore; they're actual medicines being tested in patients.

What Makes Isomorphic Labs Different From Other AI Drug Companies?

Isomorphic Labs spun off from Google DeepMind in 2021 with a radical idea: treat drug discovery like a computational problem rather than a lab-based guessing game. While traditional pharmaceutical research relies on years of experimentation, Isomorphic uses AI to predict how molecules will interact with disease-causing proteins before anything ever touches a test tube.

The company's flagship product is called the Isomorphic Labs Drug Design Engine, or IsoDDE. According to the company, IsoDDE performs some tasks more than twice as well as AlphaFold 3, DeepMind's latest protein-prediction model. Here's what makes it powerful: when researchers hunt for drug targets, they look for tiny entry points on proteins called binding pockets. IsoDDE can identify these pockets and predict which molecules will stick to them with significantly higher accuracy than previous methods.

The improvements are substantial. AlphaFold 3, released in May 2024, achieved at least a 50% accuracy improvement over earlier methods for predicting how proteins interact with small molecules, the building blocks of most drugs. In some cases, it doubled prediction accuracy. IsoDDE builds on this foundation and goes further, requiring relatively limited data about a protein to search for binding pockets, which reduces the preparatory work in pharmaceutical projects.

How Does AI Actually Speed Up Drug Discovery?

• Protein Structure Prediction: AlphaFold can predict the 3D shape of proteins from their amino acid sequence with near-experimental accuracy, a task that historically required years of manual work in laboratories.
• Binding Affinity Forecasting: IsoDDE predicts how strongly a drug compound will attach to its target protein, allowing researchers to identify the most promising candidates before lab testing.
• Molecular Interaction Modeling: The system can predict how proteins interact with DNA, RNA, and small molecules, enabling researchers to design drugs computationally and test only the most promising ones in the lab.

Traditional drug development is brutally expensive and slow. Bringing a new therapy to market typically takes over a decade and costs billions of dollars, with only roughly a 10% chance that a candidate entering clinical trials will ultimately be approved. AI promises to compress this timeline by eliminating much of the trial-and-error phase.

Isomorphic's strategy is to use generative AI models to explore the vast universe of possible protein and chemical interactions, then move only the most computationally promising candidates into physical labs and eventually human trials. This AI-first approach breaks from the traditional workflow where researchers work in silos on specific targets.

Why Is This Funding Round Significant Right Now?

The $2 billion raise comes as Isomorphic Labs is staffing up and preparing to dose patients in clinical trials of its AI-created oncology candidates. These represent some of the first therapies engineered with deep-learning protein models to reach human testing, making this a watershed moment for the entire field.

The company's pipeline focuses on oncology and immunology, two of the most challenging areas in drug development. Isomorphic has also secured major partnerships with pharmaceutical giants including Novartis, Eli Lilly, and Johnson and Johnson, validating its technology with real-world collaborations. A previous funding round in March 2025 brought in $600 million, but this new $2 billion round reflects accelerating momentum.

"Isomorphic's new engine is on the scale of an AlphaFold 4," according to expert commentary cited in the company's materials, suggesting the technology represents a generational leap beyond even the latest public models.

Scientific expert commentary, cited in Isomorphic Labs analysis

Thrive Capital, which is also a major backer of OpenAI, leading this round signals that top-tier venture investors see AI drug discovery as a transformative opportunity comparable to large language models. Alphabet's continued investment also demonstrates the parent company's confidence in the spinoff's potential.

What Will Isomorphic Do With the $2 Billion?

According to reports, Isomorphic Labs plans to use the funding to enhance IsoDDE and expand its international presence. This suggests the company is preparing to scale beyond its current operations and potentially establish research and development centers in new markets.

The timing aligns with the company's clinical trial progress. By staffing up now, Isomorphic can accelerate the development of its oncology and immunology candidates while simultaneously building out the infrastructure needed to support partnerships with major pharmaceutical companies.

Demis Hassabis, who leads Isomorphic Labs, has framed the company's mission ambitiously: solving "all disease with the help of AI". While that's a long-term vision, the immediate focus is proving that AI-designed drugs can actually work in human patients, which would validate the entire approach and potentially unlock billions in value across the pharmaceutical industry.

The convergence of breakthrough AI models, substantial funding, and real clinical trials suggests we're entering a new era where computational biology and machine learning fundamentally reshape how medicines are discovered and developed. For patients waiting for treatments to diseases that have resisted traditional drug development, this shift could mean faster access to new therapies.