Latest from MIT Tech Review – AI for protein folding

By the end of 2020, DeepMind, the UK-based artificial-intelligence lab, had already produced many impressive achievements in AI. Still, when the group’s program for predicting protein folding was released in November of that year, biologists were shocked by how well it worked. 

Nearly everything your body does, it does with proteins. Understanding what individual proteins do is therefore crucial for most drug development and for understanding many diseases. And what a protein does is determined by its three-dimensional shape.

A protein is made up of a ribbon of amino acids, which folds up into a knot of complex twists and twirls. Determining that shape—and thus the protein’s function—can take months in a lab. For years, scientists have tried computerized prediction methods to make the process easier. But no technique ever came close to matching the accuracy achieved by humans. 

That changed with DeepMind’s AlphaFold2. The software, which uses an AI technique called deep learning, can predict the shape of proteins to the nearest atom, the first time a computer has matched the slow but accurate techniques used in the lab. 

Scientific teams around the world have started using it for research on cancer, antibiotic resistance, and covid-19. DeepMind has also set up a public database that it’s filling with protein structures as AlphaFold2 predicts them. It currently has around 800,000 entries, and DeepMind says it will add more than 100 million—nearly every protein known to science—in the next year.  

DeepMind has spun off this work into a company called Isomorphic Labs, which it says will collaborate with existing biotech and pharma companies. The true impact of AlphaFold2 may take a year or two to be clear, but its potential is rapidly unfolding in labs around the world.

Related work from others:  Latest from MIT : Generating new molecules with graph grammar

As part of our 10 Breakthrough Technologies series, explore how DeepMind shifted from playing games to solving one of the hardest problems in science.

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