Having risen to fame on its superhuman performance at playing games, the artificial intelligence (AI) group DeepMind has cracked a serious scientific problem that has stumped researchers for half a century.
With its latest AI program, AlphaFold, the company and research laboratory showed it can predict how proteins fold into 3D shapes, a fiendishly complex process that is fundamental to understanding the biological machinery of life.
Independent scientists said the breakthrough would help researchers tease apart the mechanisms that drive some diseases and pave the way for designer medicines, more nutritious crops and “green enzymes” that can break down plastic pollution.
DeepMind said it had started work with a handful of scientific groups and would focus initially on malaria, sleeping sickness and leishmaniasis, a parasitic disease.
“It marks an exciting moment for the field,” DeepMind founder and chief executive Demis Hassabis said. “These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems.”
Royal Society president Venki Ramakrishnan called the work “a stunning advance” that occurred “decades before many people in the field would have predicted.”
DeepMind is best known for its run of human-trouncing programs that achieved supremacy in chess, go, Starcraft II and old-school Atari classics. The games provided a training ground for programs that, once powerful enough, would be unleashed on real-world problems.
Protein folding has been a grand challenge in biology for 50 years. An arcane form of molecular origami, its importance is hard to overstate. When researchers know how a protein folds up, they can start to uncover what it does. Scientists have identified more than 200 million proteins, but structures are known for only a fraction of them. Traditionally, the shapes are discovered through meticulous lab work that can take years.
Proteins are chains of amino acids that can twist and bend into a mind-boggling variety of shapes: a googol cubed, or 1 followed by 300 zeros.
To learn how proteins fold, researchers at DeepMind trained their algorithm on a public database containing about 170,000 protein sequences and their shapes.
DeepMind put AlphaFold through its paces by entering it for a biennial “protein Olympics” — the Critical Assessment of Protein Structure Prediction.
AlphaFold reached an accuracy comparable to the time-consuming lab-based methods. It had a median score of 92.5 out of 100, with 90 being the equivalent to experimental methods. For the hardest proteins, the median score fell, but only marginally to 87.
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