How Isomorphic Labs 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 variety of other types of biomolecules, which involves nucleic acids, small molecule ligands, ions, and other proteins. AlphaFold3 introduced a way to model the rest of these cellular biomolecules as part of a single framework. So all of a sudden, we have a single model that can model all of these interactions all at the same time.

That said, in the years since the AF3 release, multiple groups have evaluated it along the axis of pocket novelty. And you could see that as the pocket distance grows away from the training set, the model performance decreases. So if you define the success as “how well did the model actually fold this particular ligand with this particular protein,” as those systems become more novel, you can see a decline in performance.

But for drug discovery, ideally we do want to pursue novel mechanisms of action, which might involve targeting a never-before-observed pocket. And so it is absolutely important for us to have our models generalize to these regions that are distant from training.

How does the Isomorphic Drug Design Engine (IsoDDE) address these limitations, and what exactly is it predicting?

Stecuła: It takes a lot more than just structure prediction to create a molecule that will ultimately become a drug. You don’t just need to predict where the ligand binds with the protein, but also potentially how it binds, how tightly it binds, and a plethora of other properties about the ligand and how the ligand interacts with the rest of the…