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AI-Designed Drugs by a DeepMind Spinoff Are Headed to Human Trials

By Wired by By Wired
April 24, 2026
Home AI & ML
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Google DeepMind’s AlphaFold has already revolutionized scientists’ understanding of proteins. Now, the ability of the platform to design safe and effective drugs is about to be put to the test.

Isomorphic Labs, the UK-based biotech spinoff of Google DeepMind, will soon begin human trials of drugs designed by its Nobel Prize–winning AI technology. “We’re gearing up to go into the clinic,” Isomorphic Labs president Max Jaderberg said on April 16 at WIRED Health in London. “It’s going to be a very exciting moment as we go into clinical trials and start seeing the efficacy of these molecules.”

Jaderberg did not elaborate on the timeline, but it’s later than the company had planned to initiate human studies. Last year, CEO Demis Hassabis said it would have AI-designed drugs in clinical trials by the end of 2025.

Isomorphic Labs was founded in 2021 as a spinoff from Alphabet’s AI research subsidiary, Google DeepMind. The company uses DeepMind’s AlphaFold, a groundbreaking AI platform that predicts protein structures, for drug discovery.

Built from 20 different amino acids, proteins are essential for all living organisms. Long strings of amino acids link together and fold up to make a protein’s three-dimensional structure, which dictates the protein’s function. Researchers had tried to predict protein structures since the 1970s, but this was a painstaking process given the astronomically high number of possible shapes a protein chain can take.

That changed in 2020, when DeepMind’s Hassabis and John Jumper presented stunning results from AlphaFold 2, which uses deep-learning techniques. A year later, the company released an open-source version of AlphaFold available to anyone.

In 2024, DeepMind and Isomorphic Labs released AlphaFold 3, which advanced scientists’ understanding of proteins even further. It moved beyond modeling proteins in isolation to predicting other important molecules, such as DNA and RNA, and their interactions with proteins.

“This is exactly what you need for drug discovery: You need to see how a small molecule is going to bind to a drug, how strongly, and also what else it might bind to,” Hassabis told WIRED at the time.

Since its release, the AlphaFold platform has been able to predict the structure of virtually all the 200 million proteins known to researchers and has been used by more than 2 million people from 190 countries. The breakthrough earned Hassabis and Jumper the Nobel Prize for chemistry in 2024, with the Nobel committee noting that AlphaFold has enabled a number of scientific applications, including a better understanding of antibiotic resistance and the creation of images of enzymes that can decompose plastic.

Earlier this year, Isomorphic Labs announced an even more powerful tool, what it calls IsoDDE, its proprietary drug-design engine. In a technical paper, the company touts that the platform more than doubles the accuracy of AlphaFold 3.

The startup has formed partnerships with Eli Lilly and Novartis to work together on AI drug discovery and is also advancing its own “broad and exciting pipeline of new medicines” in oncology and immunology, Jaderberg said.

“The exciting thing about the molecules that we’re designing is because we have so much more of an understanding about how these molecules work, we’ve engineered them to be very, very potent,” Jaderberg told the audience at WIRED Health. “You can take them at a much lower dose, and they’ll have lower side effects, off target effects.”

Last year, Isomorphic appointed a chief medical officer and announced it had raised $600 million in its first funding round to gear up for clinical trials. Meanwhile, the company has been building a clinical development team. Its mission is to “solve all disease.”

“It’s a crazy mission,” Jaderberg said. “But we really mean it. We say it with a straight face, because we believe this should be possible.”



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Tags: Artificial Intelligencebiotechdeepmindgooglemedicinewired health
By Wired

By Wired

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