- Published on May 20, 2022
- In AI Features
DeepMind’s AlphaFold 2 is half of the story
The idea was if I give you a sequence of amino acids, can you predict what will be the structure or the shape that it will take in the 3D space?
Image by Analytics India Magazine
- By Amit Naik
Last year, Google’s research arm DeepMind released an open-source version of its deep-learning neural network AlphaFold 2, which essentially solved the 50-year-old grand challenge problem of protein folding. This computational method achieved higher prediction performance than much more expensive experimental methods like X-ray crystallography.
What’s the big deal?
As shown below, DeepMind’s AlphaFold 2 algorithm significantly outperformed other teams at the CASP14 protein-folding contest – and its previous version’s performance at the last CASP.
In 2018, its previous version achieved a score of 58 on the hardest class of proteins. The second generation of AlphaFold achieved a score of 87, which is a huge improvement – 26 points better than
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Amit Naik
Amit Raja Naik is Senior Editorial Producer – Live Shows at AIM Network, driving India’s most influential AI and technology conversations. He leads content, narrative design, and visual storytelling, engaging with leaders, innovators, and policymakers to advance how technology impacts businesses, governance, and society.
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