How a Google DeepMind Spin-off Hunts Hidden Drug Targets
IEEE Spectrum AI Eliza Strickland
Isomorphic Labs, a Google DeepMind spin-off, developed the Isomorphic Drug Design Engine to identify hidden protein binding pockets and predict drug-protein interactions beyond what existing AlphaFold models can achieve. The system successfully predicted a previously unknown cryptic pocket on cereblon protein that was only recently published in a Nature paper in January. The tool aims to make more disease-associated proteins tractable as drug targets by modeling interactions with small molecules, antibodies, peptides, and other therapeutic compounds.
Why it matters
For more than a decade, artificial intelligence has been touted as a way to dramatically accelerate drug discovery. Yet despite billions of dollars in investment, relatively few AI-designed medicines have made it to patients. That’s partially because the timelines for careful drug testing can’t be easily compressed—and partially because drug development is just really hard. Isomorphic Labs, the Google DeepMind spin-off that’s building on DeepMind’s Nobel Prize-winning work on protein structure prediction, may be making the most progress. The company has signed major drug-discovery partnerships with Novartis and Eli Lilly and recently raised US $2.1 billion in funding. In February, it published a technical report describing its new Isomorphic Drug Design Engine, a system created to discover the “pockets” on proteins where drugs can bind and in general to predict how proteins and drug molecules interact. IEEE Spectrum spoke with Adrian Stecuła, a group leader in the machine learning orga
