The artificial intelligence system developed by the scientists from DeepMind has been awarded a $3 million prize.
The co-founder and CEO of DeepMind, as well as a senior staff research scientist at the company, received the prize.
Live Science previously reported that the open-source program makes its predictions based on the sequence of a proteins's amino acids. Individual units link up in a chain and are folded into a shape. Being able to infer the shape of a protein's amino acid sequence is incredibly powerful, because the 3D structure of aProtein dictates what thatProtein can do.
Leading researchers in the fields of fundamental physics, life sciences and mathematics are the recipients of the breakthrough prizes. Each prize has a $3 million award supplied by founding sponsors.
Scientists win a $3 million breakthrough prize for their work.
Predicting their 3D structure from the sequence of their amino acids is central to understanding the workings of life. Hassabis and Jumper built a deep learning system with their team at DeepMind.
According to Live Science, the DeepMind team has compiled a database of 200 million structures of different types of organisms. The database contains nearly all known science-related proteins.
The artificial intelligence system was able to assemble these shapes by studying existing databases. The X-ray crystallography technique used to visualize these structures uses X-rays to measure how the rays diffract.
AlphaFold was able to identify patterns between the amino acid sequence and the final shape. The artificial intelligence used a neural network to iteratively improve its ability to predict both known and unknown structures.
Hassabis wrote that AlphaFold has been used for everything from understanding diseases to protecting honey bees to looking deeper into the origins of life.
"As pioneers in the emerging field of 'digital biology', we're excited to see the huge potential of artificial intelligence starting to be realised as one of humanity's most useful tools for furthering scientific discovery and understanding the fundamental mechanisms of life," he wrote.
It was originally published on Live Science
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