> This was tested on Google Cloud with a machine using the nvidia-gpu-cloud-image with 12 vCPUs, 85 GB of RAM, a 100 GB boot disk, the databases on an additional 3 TB disk, and an A100 GPU.
This is amazingly detailed for a researcher who wants to follow in the track and also Apache licensed, which is one road-bump out of the way for a commercial enterprise, like an actual drug manufacturer who wants to burn some money trying this out.
edit: said the last part too fast, the code has a "the AlphaFold parameters are made available for non-commercial use only under the terms of the CC BY-NC 4.0 license"
It's quite unclear what value this will have to pharma; personally I doubt this has any direct applications (and I'm one of the few people in the world that can say that with deep authority).
I don't know what you mean you can't put your thesis work into git. Is it a physical thing? Too big for git?
Still, even if structure determination is not on the "critical path", it IS a big barrier that has (started) to fall now.
Personally, I've found over decades that academic papers like that are far less useful to me than a github project and downloadable data that I can inspect, run and modify on my own. Other folks I know could read that paper and write the code in a day, I always wish I could do that.
The underlying sequence datasets include PDB strucrures and sequences, and how those map to large collections of sequences with no known structure (no surprise). Each of those datasets represents decades of thousands of scientists work, along with programmers and admins who kept the databases running for decades with very little grant money (funding long-term databases is something NIH hated to do until recently).