DeepMind set its sights on protein folding after its AlphaGo program famously beat Lee Sedol, a champion Go player, in 2016. While games have proved to be a good testing ground for the group’s AI programs, high scores are not their ultimate goal. “It’s never been about cracking Go or Atari, it’s about developing algorithms for problems exactly like protein folding,” Hassabis said.
On its first foray into the competition, AlphaFold topped a table of 98 entrants, predicting the most accurate structure for 25 out of 43 proteins, compared with three out of 43 for the second placed team in the same category.
To build AlphaFold, DeepMind trained a neural network on thousands of known proteins until it could predict 3D structures from amino acids alone. Given a new protein to work on, AlphaFold uses the neural network to predict the distances between pairs of amino acids, and the angles between the chemical bonds that connect them. In a second step, AlphaFold tweaks the draft structure to find the most energy-efficient arrangement. The program took a fortnight to predict its first protein structures, but now rattles them out in a couple of hours.
https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins
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