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Comment by mhrmsn | Hacker News Reader

Also no commercial use, from the paper:

> AlphaFold 3 will be available as a non-commercial usage only server at https://www.alphafoldserver.com, with restrictions on allowed ligands and covalent modifications. Pseudocode describing the algorithms is available in the Supplementary Information. Code is not provided.

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How easy/hard would be for the scientific community to come up with an "OpenFold" model which is pretty much AF3 but fully open source and without restrictions in it?

I can image training will be expensive, but I don't think it will be at a GPT-4 level of expensive.

already did it, https://openfold.io/ https://github.com/aqlaboratory/openfold https://www.biorxiv.org/content/10.1101/2022.11.20.517210v1 https://lupoglaz.github.io/OpenFold2/ https://www.biospace.com/article/releases/openfold-biotech-a...

I really have to emphasize that transformers have literally transformed science in only a few years. Truly extraordinary.

Oh, nice! Thanks for sharing.

If you need to submit to their server, I don't know who would use it for commercial reasons anyway. Most biotech startups and pharma companies are very careful about entering sequences into online tools like this.

What's the point in that that - I mean who does non-commercial drug research?

Academia

Public universities?

Yes, because that's going to stop competitors.. it's why they didn't release code I guess.

This is yet another large part of a biotech related Gutenberg moment.

The DeepMind team was essentially forced to publish and release an earlier iteration of AlphaFold after the Rosetta team effectively duplicated their work and published a paper about it in Science. Meanwhile, the Rosetta team just published a similar work about co-folding ligands and proteins in Science a few weeks ago. These are hardly the only teams working in this space - I would expect progress to be very fast in the next few years.

How much has changed- I talked with David Baker at CASP around 2003 and he said at the time, while Rosetta was the best modeller, every time they updated its models with newly determined structures, its predictions got worse :)

It's kind of amazing in retrospect that it was possible to (occasionally) produce very good predictions 20 years ago with at least an order of magnitude smaller training set. I'm very curious whether DeepMind has tried trimming the inputs back to an earlier cutoff point and re-training their models - assuming the same computing technologies were available, how well would their methods have worked a decade or two ago? Was there an inflection point somewhere?

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