How a Google DeepMind Spin-off Hunts Hidden Drug Targets
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 organization at Isomorphic Labs, about how close AI may be to becoming a practical tool for designing new medicines. Going Beyond AlphaFold AlphaFold2 and AlphaFold3 were massive leaps forward for computational biology. Why weren’t those models sufficient for actually designing drugs? Adrian Stecuła: AlphaFold2 was eventually recognized with the Nobel Prize , because it arguably solved the problem of protein folding. But proteins don’t exist in a vacuum, right? They interact with a wide variet…
