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An animation of a protein modeled by AlphaFold 2.

The Google sister company devoted to artificial general intelligence parlayed its technology into a biomedical spin-off.

What’s new: DeepMind launched a startup called Isomorphic. The new company aims to build its business on AlphaFold 2, an ensemble of neural networks that finds the shapes of protein molecules, which determine their biological function. The company is hiring experts in AI, biology, medicinal chemistry, biophysics, and engineering.

How it works: Like DeepMind, Isomorphic is a subsidiary of Google’s parent company Alphabet. DeepMind CEO Demis Hassabis also leads the London-based spin-off.

  • Isomorphic will build predictive models to investigate the medical potential of proteins, the interactions between them, and the ways they bind to receptors in the body.
  • The company likely will sell its services to pharmaceutical companies rather than developing drugs itself, Hassabis told the healthcare website Stat.

Behind the news: AlphaFold 2 has analyzed the shapes of over 98 percent of proteins in the human body. It remains for scientists to validate its output through lab experiments.

  • AlphaFold debuted in 2018, when it won an annual contest for predicting protein shapes.
  • A revised version won again in 2020 with an average error comparable to the width of an atom.
  • DeepMind opened the system in July along with databases that detail the structure of hundreds of thousands of proteins.

Why it matters: Just 6.2 percent of drug candidates make it through clinical trials to market, and the cost of developing a successful medicine costs $1.3 billion on average. Isomorphic could wring trial and error out of the process, boosting success rates, cutting costs, and enriching drug-company customers.

We’re thinking: AlphaFold 2 is a big step forward for biomedicine, and deep learning promises further progress in areas like protein-protein interaction (how does a potential treatment interact with a target protein?) and protein dynamics (protein shapes aren’t static, and their motion can affect their properties). Much work by many determined researchers lies ahead to bridge the gap between lab and clinic.


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