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AlphaFold is DeepMinds latest breakthrough addressing the protein folding problem. Using an advanced Deep Learning architecture that achieves end-to-end learning of protein structures, this work is arguably one of the most influential papers of this decade and is likely to spark enormous advanced in computational biology and protein design. This video covers the entire architecture of the model as well as training principles that led to the incredible results of AlphaFold2!

AlphaFold Nature paper: https://www.nature.com/articles/s41586-021-03828-1
AlphaFold Codebase: https://github.com/deepmind/alphafold
Work from the Baker lab: https://www.bakerlab.org/
Fabian Fuchs' amazing blog on equivariance: https://fabianfuchsml.github.io/alphafold2/
Ongoing Open Source effort to reproduce AlphaFold: https://github.com/lucidrains/alphafold2

::Chapters::
00:00 Intro
02:28 The Protein Folding Problem
05:29 AlphaFold1 revisited
06:10 Multiple Sequence Alignments (MSA)
08:10 Distograms
12:29 AlphaFold2
14:52 The Evoformer
19:07 The Structure Module
28:13 Zooming out: looking at the future

(From Youtube)