I found a recent review with some others listed here [2]. It has a nice overview of the process too!
Forgot to answer your other questions. I'm not up to date on the structure-based drug design workflow but back when I did similar work (5 years ago) there definitely were rudimentary systems for generating molecules and docking them. It may have improved significantly since then. But I would probably characterize it as a problem it itself for sure.
Your other question is a VERY good one. Proteins usually fold into whatever may be most favorable based on the sequence, and it mostly stays consistent once it does. However, they are very flexible and structures solved by EM or x-ray crystallography are like a photograph of bird flapping its wings: you will see the wings in a position, and if you happen to have a few birds in the photograph, you might get a sense of where those wings can move to, but it's never going to be perfect. But like wings, proteins usually still have a limited amount of movement. There are other types that are much harder to understand that have less structure, but globular proteins that bind to drugs like this are usually pretty well-predicted by the snapshots we can get.
Researchers use docking software to run libraries of existing drugs as well as design never-seen-before drugs out of the enzyme protein's active-site pocket.
These operations have been performed extensively this year (by research groups all over the world) on covid's main protease enzyme as well as the spike-ACE2 interface, for example.
My take: Since 0.1% of proteins whose amino acids have been sequenced have ever seen a crystal structure (i.e. the folded model) generated of them. an automated approach to 3D model generation 1) will have enormous implications on drug development, and 2) will most likely come from a new and very different generation of drug developers, who don't have a lot in common with the generation that produced the tweet pasted above.
Once you have a candidate, it is very useful to determine the structure of the protein together with the drug candidate. There you can see how it binds, and can make some educated guesses on how to change the molecule to make it bind better, or to improve other aspects without making it bind worse.
Determing the protein fold from scratch without experimental data is impressive, but it doesn't have an immediate use for drug development. But a few steps further and it could certainly help if you can also predict which molecules bind to the protein structure.
I would strongly recommend the following blog post from Derek Lowe to put the importance of this into context for drug development:
https://blogs.sciencemag.org/pipeline/archives/2020/12/01/th...
Once Alpha Fold or future programs get better with side chain modeling (not even for the entire protein just some parts), they will also allow complete computer based design of new antibodies against any target of choice (this is currently only possible through experiments and the technologies that allow this are all heavily patented and proprietary).
Variations of AlphaFold will also be significantly useful in research in general, potentially becoming fundamental enough that every project working with proteins might reach out to this tool like they reach out to to say mass spectrometry or flow cytometry.
Protein folding will help develop candidates faster. That's good. But it won't seriously help find the right targets faster, so I don't expect a substantial speedup in overall drug development times.
What the folding model essentially does, is confirm that the modeled folded protein has the correctly modeled binding energies for each individual atom. Making modeling induced fit, and conformationally dynamic and difficult to drug proteins, more easy to model and find the correct ligands that bind to them.
This will certainly help. But we shouldn't expect drugs to be discovered in weeks instead of years, since the bit that usually takes a year or more (the clinical trials) isn't changed here.
As every disease has a mechanism via a protein channel, and all cells are made of proteins, doesn't this open the possibility of curing nearly any disease for which we understand it's mechanism and for which we can conceive of a protein shape to block or rebuild the tissue damages caused by such a disease? Delivery will still be an issue, but I don't see how this couldn't be used to prevent metastasis in tumors by custom created blocking proteins, for example.
Who know what else is flooding around.