AlphaFold and drug discovery

Ellen Callaway, AlphaFold found thousands of possible psychedelics. Will its predictions help drug discovery? Nature, Jan. 18, 2024.

Researchers have used the protein-structure-prediction tool AlphaFold to identify hundreds of thousands of potential new psychedelic molecules — which could help to develop new kinds of antidepressant. The research shows, for the first time, that AlphaFold predictions — available at the touch of a button — can be just as useful for drug discovery as experimentally derived protein structures, which can take months, or even years, to determine.

The development is a boost for AlphaFold, the artificial-intelligence (AI) tool developed by DeepMind in London that has been a game changer in biology. The public AlphaFold database holds structure predictions for nearly every known protein. Protein structures of molecules implicated in disease are used in the pharmaceutical industry to identify and improve promising medicines. But some scientists had been starting to doubt whether AlphaFold’s predictions could stand in for gold standard experimental models in the hunt for new drugs.

“AlphaFold is an absolute revolution. If we have a good structure, we should be able to use it for drug design,” says Jens Carlsson, a computational chemist at the University of Uppsala in Sweden.

The article presents material showing that AlphaFold is useful gor drug discovery in some cases, but not others.

Shoichet agrees that AlphaFold predictions are not universally useful. “There were a lot of models that we didn’t even try because we thought they were so bad,” he says. But he estimates that in about one-third of cases, an AlphaFold structure could jump-start a project. “Compared to actually going out and getting a new structure, you could advance the project by a couple of years and that’s huge,” he says.

That is the goal of Isomorphic Labs, DeepMind’s drug-discovery spin-off in London. On 7 January, the company announced deals worth a minimum of US$82.5 million — and up to $2.9 billion if business targets are met — to hunt for drugs on behalf of pharmaceutical giants Novartis and Eli Lilly using machine-learning tools such as AlphaFold.

The company says that the work will be aided by a new version of AlphaFold that can predict the structures of proteins when they are bound to drugs and other interacting molecules. DeepMind has not yet said when — or whether — the update will be made available to researchers, as earlier versions of AlphaFold have been. A competing tool called RoseTTAFold All-Atom3 will be made available soon by its developers.

Such tools won’t fully replace experiments, scientists say, but their potential to help find new drugs shouldn’t be discounted. “There’s a lot of people that want AlphaFold to do everything, and a lot of structural biologists want to find reasons to say we are still needed,” says Carlsson. “Finding the right balance is difficult.”