Isomorphic Labs Says AI Will Design All Drugs One Day

Officially launched on November 2, 2026.

Isomorphic Labs president Max Jaderberg delivered a bold forecast at the Bloomberg Tech conference in London on November 2, 2026: artificial intelligence will design every medicine in the future. The only uncertainty, he said, is how quickly that transformation will occur. The prediction comes as the Google DeepMind spinout advances AI-powered drug discovery programs with pharmaceutical giants and pursues a potential $40 billion valuation.

Current Status and Clinical Timeline

Officially launched as a statement at Bloomberg Tech on November 2, 2026, Jaderberg's vision reflects Isomorphic's ongoing preclinical work rather than an immediate product release. The company remains in its "first innings," according to Jaderberg, with programs still in preclinical trials. CEO Demis Hassabis had previously indicated the startup expected to move into its first clinical trials by the end of 2026, a milestone the company is working toward as it builds AI models for internal drug development and partnerships with Eli Lilly and Novartis.

Founded in 2021 and spun out of Alphabet's Google DeepMind, Isomorphic has raised approximately $2.7 billion from backers including Alphabet and Thrive Capital. Bloomberg reported in October 2026 that the startup entered talks for a new funding round that could value it at $40 billion or more, though Isomorphic has not disclosed its current valuation since announcing fresh funding in May 2026.

Vision for AI-Designed Medicines

Jaderberg characterized traditional drug development as largely trial-and-error, producing what he called "low-hanging fruit." He argued that humanity has not been particularly effective at creating medicines, calling existing medications "a miracle" given the limitations of conventional methods. His statement that all drugs will eventually be AI-designed reflects confidence in computational approaches surpassing human-led discovery processes.

The executive emphasized urgency in his remarks: "This will happen. All drugs in the future will be AI-designed. There's no question in my mind. I just don't want that to take 20 years or 50 years to happen passively." This timeline suggests Isomorphic aims to accelerate adoption across the pharmaceutical industry within the next decade rather than allowing gradual, organic adoption over half a century.

Technology and Focus Areas

Isomorphic builds on the breakthrough protein-folding model AlphaFold, developed at Google DeepMind. The company creates AI models both for its own drug development programs and to power initiatives at partner biopharma companies. Its internal programs focus on three therapeutic areas:

• Cancer treatments
• Immune system disorders
• Inflammation-related conditions

Demis Hassabis, who founded Isomorphic while serving as Google DeepMind's CEO, moved into new roles as DeepMind's chair and Alphabet's chief scientist in August 2026. He remains Isomorphic's CEO, maintaining leadership across the drug discovery venture while taking on broader responsibilities within Alphabet's AI ecosystem.

Competitive Landscape

Isomorphic operates in an increasingly crowded field of AI life science companies. Anthropic acquired biotech startup Coefficient Bio earlier in 2026 and released Claude Science in beta for AI-powered scientific research. OpenAI-backed Chai Discovery is raising venture capital to compete directly with Isomorphic on AI drug discovery, indicating significant investor interest in applying large language models and machine learning to pharmaceutical development.

What This Means

Jaderberg's prediction signals growing confidence that AI will fundamentally reshape drug discovery within a generation. With preclinical programs underway, multi-billion-dollar partnerships with major pharmaceutical companies, and a potential $40 billion valuation, Isomorphic Labs positions itself at the center of this transformation. Whether the industry achieves full AI-driven drug design in 10 years or 50 will depend on clinical trial outcomes, regulatory frameworks, and the pace at which traditional pharmaceutical companies adopt computational methods. The stakes are high: faster, more accurate drug discovery could accelerate treatments for diseases that currently lack effective therapies.