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Comment by gopalv | Hacker News Reader

> The total download size is around 428 GB and the total size when unzipped is 2.2 TB. Please make sure you have a large enough hard drive space, bandwidth and time to download.

> 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"

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Yes, all science should be communicated in the form of an academic paper wiht a supporting git repo and quickly downloadable dataset and a fast path to reproducing the work. That would be a huge change from the establishment.

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).

Who benefits from this work?

Primarily the community that previously depended on homology models.

Surely not all science. Just as well Dirac wasn't required to communicate that way the equation that fundamentally underlies the phenomenon discussed, and you couldn't put the unique facility my thesis work pioneered into git! I do highly approve of publishing software and data where possible, of course, since before Free Software needed to be coined, and it's much easier now.

If you're just publishing equations, you should have an associated notebook which executes the equations.

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?

Equations are math. They can be used analytically with pen and paper. No need to turn them into code.

Why wouldn't this have much value to pharma? Is it because its application is actually really limited in scope?

there are research groups this would be useful for but structures are not on the critical path to drug discovery or approval.

Out of (probably overoptimistic :) ) curiosity, what do you see are the critical paths?

I've been doing protein pharma research and the structure is only a first step, then years of figuring out the kinematics and dynamics of the protein, figuring out how it works, how all the natural ligands bind and affect the kinematics.. and only after all that you might conceivably start to engineer drug compounds (unless you bootstrap by a natural ligand to tweak "randomly", but then again, that's how pharma development traditionally works).

Still, even if structure determination is not on the "critical path", it IS a big barrier that has (started) to fall now.

Initial molecule generation and FDA approval.

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