Isomorphic Labs, the biotech spinout founded by Nobel laureate Demis Hassabis, just secured $2.1 billion in Series B funding to bring AI-designed drugs to human trials. The funding round, led by Thrive Capital and backed by Google, Alphabet, and sovereign wealth funds, represents the second-largest biotech investment ever. Yet behind this celebration of scientific progress lies a harder question: if AI helps us discover cures faster, who will actually get access to them?

The story of Isomorphic Labs begins with one of the most remarkable scientific achievements of the past decade. Demis Hassabis, CEO of Google DeepMind, spent years obsessed with a single problem: how proteins fold. Proteins are the machinery of life, performing nearly every function in our bodies. Their three-dimensional shape determines what they can do, yet predicting that shape from a protein's amino acid sequence had stumped scientists for 50 years.

In 2020, DeepMind released AlphaFold2, an artificial intelligence system that could predict protein structures with atomic-level accuracy in minutes, a task that once took teams of scientists months or years. The breakthrough was so significant that Hassabis and collaborator John Jumper shared the 2024 Nobel Prize in Chemistry for this work.

How Did AlphaFold Transform Scientific Research?

What made AlphaFold revolutionary wasn't just the speed or accuracy. It was what Hassabis did next. Instead of keeping the technology proprietary, DeepMind released predicted structures for over 214 million proteins into a free, public database. More than three million scientists now use AlphaFold, from plant biologists working on climate-resilient crops to researchers investigating neglected diseases like malaria and Chagas disease.

For underfunded researchers, this was transformative. Scientists working on diseases that pharmaceutical companies ignore because patients are too poor could suddenly access high-quality protein structure predictions at no cost. They could "jump straight to the problem they're interested in" instead of spending years on expensive crystallization experiments.

• Speed Improvement: AlphaFold reduced protein structure prediction from months or years to minutes or hours, accelerating research timelines across biology
• Global Access: The free public database enabled researchers in under-resourced institutions to conduct work previously available only to well-funded labs
• Scale: The system predicted structures for over 214 million proteins, compared to the thousands that had been experimentally determined before

Where Does the Profit Motive Enter the Picture?

But AlphaFold is only the first step in drug development. Knowing a protein's structure is one piece of the puzzle. Designing a molecule that binds to the right part of that structure, strongly and selectively, without causing toxicity elsewhere in the body, is an enormous search problem. This is where Isomorphic Labs enters the picture.

Isomorphic Labs built on AlphaFold's foundation to create IsoDDE, a unified AI drug design engine. The system can propose chemical compounds that might bind to a target protein, simulate how strongly they bind, and rapidly check predicted interactions against thousands of other proteins to minimize side effects. Instead of testing a handful of molecules in a wet lab, researchers can explore thousands or millions virtually and only validate the most promising candidates experimentally.

The results have been striking. IsoDDE rediscovered cereblon's second binding pocket from sequence alone, an achievement that took human researchers 15 years to confirm experimentally. The company expects its first wholly-owned drug candidate to enter human trials by the end of 2026, with plans to expand its pipeline and continue improving the underlying technology.

"Almost every drug developed from now on will probably have used AlphaFold at some stage in its pipeline," according to feedback from an industry scientist cited in the sources.

Industry scientist, pharmaceutical development

Here's where the story takes a turn. AlphaFold's database is open and free. Isomorphic Labs' eventual drugs will not be. They will be governed by patents, trade secrets, pricing strategies, reimbursement negotiations, and national health-system budgets. None of these downstream mechanisms is determined by AI. They are determined by corporate boards, regulators, trade agreements, and political choices.

Why Does Access Matter More Than Discovery?

AI can make it cheaper and faster to discover candidate drugs, but there is no automatic mechanism that converts that efficiency into fairer access. Under current incentives, it is just as likely to convert into higher margins, faster pipelines, and stronger competitive advantages for existing pharmaceutical players.

The tension is stark. AI-accelerated drug discovery, precision oncology, and bespoke gene therapies will almost certainly arrive first as premium services in well-funded health systems and private clinics, marketed as "personalized" or "longevity" solutions for those who can pay. Meanwhile, in under-resourced hospitals and rural clinics, doctors will still be fighting old battles with overstretched staff, intermittent electricity, and shortages of even basic medicines.

The same algorithms that help a multinational design its next blockbuster cancer drug could, in principle, also optimize supply chains for essential generics or support community health workers. But there is no automatic mechanism that steers AI in that direction. Without deliberate choices by regulators, funders, and companies, the benefits of these tools will naturally flow along existing fault lines of wealth and geography, deepening them rather than healing them.

Demis Hassabis has stated that the most important use of AI is to improve human health. His track record suggests he means it. He and his team built AlphaFold and chose to give it to the world for free. Yet Isomorphic Labs operates in a different ecosystem, one governed by the economics of pharmaceutical development. The question now is whether the same visionary leadership that created open-access protein structures will extend to ensuring that AI-designed drugs reach the patients who need them most, not just those who can afford them.