This is a good discussion of DeepMind's AlphaFold 2, a big breakthrough in protein folding. The details of how AlphaFold 2 works have not been published -- the video mainly discusses the January 2020 paper on the earlier version of AlphaFold, which already had world leading performance. However, it provides a good introduction both to protein folding as a physical / biological problem, as well as to AI/ML approaches.
I visited DeepMind in 2018 to give a talk on genomic prediction. I was hoping to get them interested! However, they were already focused on the protein folding problem. Most of my time there was spent discussing the latter topic with some of the AlphaFold team. They probably thought that a physicist who works on genomics might be worth talking to about protein folding, but I'm sure I learned more from them about it than vice versa...
In 2013 I blogged about a talk by Fields Medalist Stephen Smale on ML approaches to protein folding. He convinced me that ML approaches might work better than solving physics equations by brute force.
Deep neural nets excel at learning high dimensional nonlinear functions that have some internal hierarchical structure (e.g., by length scale). Protein folding falls into this category. AlphaFold was able to utilize 170k training samples and extensive information from MSA (Multiple Sequence Alignment) which gives estimates of 3D distances: see, e.g., here.