Every budding biologist learns about proteins and the amino acids that build them. Proteins are the building blocks of life, but knowing the sequence for the protein is only half of the story. How the protein folds onto itself determines what sections are exposed and can interact with other molecules, and therefore also what sections are hidden.

This is called the protein folding problemand has stumped the scientific community for about 50 years. Scores of researchers around the world are working to predict how proteins are folded, many using artificial intelligence (AI).

Biologists want to be able to predict how a protein folds because that gives insight into what it does and how it functions in the body. Geneticists and researchers have gained understanding about genes that encode for proteins, but experts have less knowledge about what happens when proteins are released to do their jobs.

One group at DeepMind, a Google AI offshoot, built an AI system that has done what others have not been able to. The group entered their algorithm, called AlphaFold, in the biennial protein-structure prediction challenge called Critical Assessment of Structure Prediction (CASP). The organizers of CASP look at the accuracy of predictions to assess how good the solutions are. The assessment is done blind, meaning the assessors dont know whose results they are looking at.

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This year, AlphaFold has come out on top, beating its past performance and others in the competition.

This is a big deal,said John Moult, who is a computational biologist at the University of Maryland in College Park and co-founded CASP in 1994, to Nature. In some sense the problem is solved.

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Research groups that dont use AI usually focus on experiments and collect data like X-ray diffraction data. One group that was trying to figure out a bacteria protein has been studying it for a decade while AlphaFold solved it in half an hour, according to Nature.

This is a problem that I was beginning to think would not get solved in my lifetime,said Janet Thornton, who is a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute and a past assessor for CASP, to Nature.

DeepMind is mostly known for its success in chess, Go and other games. Demis Hassabis, DeepMinds founder and chief executive,said to The Guardian, These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems.

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