> when your entire computational technique is built on finding analogies to known structures, what can you do when there’s no structure to compare to
Lots of people seem focused on the idea that deep networks can't do anything novel and are just like fancy search engines that find a similar example and copy it. This is not true. They do learn from much deeper low level structures in the domain they are exposed to. They can be aware of implicit correlations and constraints that are totally outside what may be recognised in the scientific understanding. Hence AlphaFold is quite capable of predicting a structure for which there is no previous direct "analogy". As long as the protein has to follow the laws of physics then AlphaFold as at least a basis to work from in successfully predicting the structure.
> It is very, very rare for knowledge of a protein’s structure to be any sort of rate-limiting step in a drug discovery project!
This and the following text are very reductive. It's like saying, back in 1945 that nuclear weapons would not be any sort of advantage in WW2 because it is very rare for weapons of mass destruction to win a war. Well yes it was rare, because they didn't exist. And so too did we not have a meaningfully accurate way to predict protein structures until AlphaFold. We've barely even begun to exploit the possible new opportunities for how to use that. And people have barely scratched the surface in adapting AlphaFold to tackle the related challenges downstream from straight up structure prediction. Predicting formation of complexes and interactions is the obvious next step and it's exactly what people are doing.
It's not to say that it will revolutionise drug development, but the author's argument here is that he is confident it will not and he really doesn't assert much evidence of that.