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DeepMind says it will release the structure of every protein known to science

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Back in December 2020, DeepMind took the world of biology by surprise when it solved a 50-year grand challenge with AlphaFold, an AI tool that predicts the structure of proteins. Last week the London-based company published full details of that tool and released its source code. Now the firm has announced that it has used its AI to predict the shapes of nearly every protein in the human body, as well as the shapes of hundreds of thousands of other proteins found in 20 of the most widely studied organisms, including yeast, the fruit fly, and the mouse. The breakthrough could allow biologists from around the world to understand diseases better and develop new drugs. So far the trove consists of 350,000 newly predicted protein structures.


The Drug Discoverer - Reflecting on DeepMind's AlphaFold artificial i

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Last month, DeepMind published the much anticipated, detailed methodology underlying the latest version of AlphaFold – the UK-based science company's powerful AI system that blew away its rivals in the latest major competition to predict the 3D structure of proteins. AlphaFold's machine learning methodology has been applied to predict structures for almost 99% of human proteins which have now been made publicly available. In this long read, I reflect on the significance of these developments for fundamental research and drug discovery. I wrote this as the ICR celebrates the 10th anniversary of its AI-enabled drug discovery knowledgebase canSAR – which features multiple approaches to predicting'druggability' as an aid to selecting drug targets and accelerating drug discovery. The coronavirus pandemic has, understandably, soaked up a lot of bandwidth when it comes to science news – but one particular non-Covid science story was able to cut through and hit the headlines in the UK and around the world. On 30 November 2020 it was announced that DeepMind – a subsidiary of Google's parent company Alphabet focusing on artificial intelligence – had made what was hailed as a huge leap towards solving one of biology's greatest remaining challenges: the ability to predict the correct, three-dimensional structures of proteins based on their constituent, one-dimensional amino acid sequences. The announcement attracted huge interest, but the expert community has been waiting for the peer-reviewed science publication. The AI methodology has now been published in the leading journal Nature and this was followed rapidly by a second Nature paper from DeepMind and collaborators at the European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), which reports the application of the most recent AlphaFold machine learning system to predict the 3D structures at scale for almost the entire human proteome – 98.5% of human proteins.


AI has cracked a problem that stumped biologists for 50 years. It's a huge deal.

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DeepMind, an AI research lab that was bought by Google and is now an independent part of Google's parent company Alphabet, announced a major breakthrough this week that one evolutionary biologist called "a game changer." "This will change medicine," the biologist, Andrei Lupas, told Nature. The breakthrough: DeepMind says its AI system, AlphaFold, has solved the "protein folding problem" -- a grand challenge of biology that has vexed scientists for 50 years. Proteins are the basic machines that get work done in your cells. They start out as strings of amino acids (imagine the beads on a necklace) but they soon fold up into a unique three-dimensional shape (imagine scrunching up the beaded necklace in your hand).


'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures

Nature

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."


'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures

#artificialintelligence

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."