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.
