Published on January 15, 2019 by

DeepMind, an AI lab & complete outsider to the field of molecular biology, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures. This is huge! DeepMind didn’t yet release the paper or code, so I browsed the CASP reports and different related papers to reverse engineer the architecture as best as I could. What I ended up doing is forking a related research papers code and repurposing it, since its very similar (2 residual networks were used). In this episode, i explain the different components of its architecture, why the protein folding problem is so important, and give some programmatic examples. Enjoy!

Code for this video (with coding challenge):
https://github.com/llSourcell/DeepFolding

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More learning resources:
https://deepmind.com/blog/alphafold/
https://moalquraishi.wordpress.com/20…
https://www.reddit.com/r/MachineLearn…
https://medium.com/syncedreview/deepm…
https://news.ycombinator.com/item?id=…

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