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Comment by singularity2001 | Hacker News Reader

singularity2001

  >>  I do think AlphaFold already has reached sufficient level of impact.

how so?

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Well I'm sure one could look at number of published papers etc, but that metric is a lot to do with hype and I see it as a lagging indicator.

A better one is seeing my grad-school friends with zero background in comp-sci or math, presenting their cell-biology results with AlphaFold in conferences and at lab meetings. They are not protein folding people either- just molecular biologists trying to present more evidence of docking partners, functional groups in their pathway of interest.

It reminds me of when Crispr came out. There were ways to edit DNA before Crispr, but its was tough to do right and required specialized knowledge. After Crispr came out, even non-specialists like me in tangential fields could get started.

In both academic and industrial settings, I've seen an initial spark of hope about AlphaFold's utility being replaced with a resignation that it's cool, but not really useful. Yet in both settings it continued as a playing card for generating interest.

There's an on-point blog-post "AI and Biology" (https://www.science.org/content/blog-post/ai-and-biology) which illustrates why AlphaFold's real breakthrough is not super actionable for creating further bio-medicinal applications in a similar vein.

That article explains why AI might not work so well further down the line biology discoveries, but I still think alphafold can really help with the development of small molecule therapies that bind to particular known targets and not to others, etc.

The thing with available ligand + protein recorded structures is that they are much, much more sparse than available protein structures themselves (which are already kinda sparse, but good enough to allow AlphaFold). Some of the commonly-used datasets for benchmarking structure-based affinity models are so biased you can get a decent AUC by only looking at the target or ligand in isolation (lol).

Docking ligands doesn't make for particularly great structures, and snapshot structures really miss out on the important dynamics.

So it's hard for me to imagine how alphafold can help with small molecule development (alphafold2 doesn't even know what small molecules are). I agree it totally sounds plausible in principle, I've been in a team where such an idea was pushed before it flopped, but in practice I feel there's much less use to extract from there than one might think.

EDIT: To not be so purely negative: I'm sure real use can be found in tinkering with AlphaFold. But I really don't think it has or will become a big deal in small drug discovery workflows. My PoV is at least somewhat educated on the matter, but of course it does not reflect the breadth of what people are doing out there.

But Crispr actually edited genes. How much of this theoretical work was real, and how much was slop? Did the grad students actually achieve confirmation of their conformational predictions?

Surprisingly, yes the predicted structures from AlphaFold had functional groups that fit with experimental data of binding partners and homologues. While I don't know whether it matched with the actual crystallization, it did match with those orthogonal experiments (these were cell biology, genetics, and molecular biology labs, not protein structure labs, so they didn't try to actually crystalize the proteins themselves).

https://www.pnas.org/doi/10.1073/pnas.2315002121

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