for context, nearly all medical biology research touts itself as a solution to the problem, but is really just a tiny component of a far larger ecosystem of research, development, and deployment.
To see how messed up things can get, take a look at Vioxx. I saw the entire life cycle of vioxx during my earlier career: from "we crystallized cox-1 and cox-2 and now we can make differential inhibitors that don't cause stomach bleeding" to clinical trials to the drug being taken off the market because the clinical trials failed to report serious problems.
So these days every time a person waves their hand and claims they solved a problem I ask what the direct and immediate effect on the actual problems will be and it also elicits an answer of "well, it's complicated... next we have to do <soandso> and <suchandsuch> before we can even put it in a human". genomics with all its claims for human health went through the same hype cycle. As did nanomedical diagnostics.
Personally I think AlphaFold proved its value from day one, by firmly establishing the knowledge (which had already been speculated) that everything you need to predict protein structures is a large enough collection of protein structures, a much larger collection of protein sequence relationships, and a collection of very savvy machine learning techniques to extract the maximum information from that to produce the most physically plausible model. It also showed that you could do all this without explicitly modelling the folding process itself, which is such a huge timesaver.
To see how messed up things can get, take a look at Vioxx. I saw the entire life cycle of vioxx during my earlier career: from "we crystallized cox-1 and cox-2 and now we can make differential inhibitors that don't cause stomach bleeding" to clinical trials to the drug being taken off the market because the clinical trials failed to report serious problems.
So these days every time a person waves their hand and claims they solved a problem I ask what the direct and immediate effect on the actual problems will be and it also elicits an answer of "well, it's complicated... next we have to do <soandso> and <suchandsuch> before we can even put it in a human". genomics with all its claims for human health went through the same hype cycle. As did nanomedical diagnostics.
Personally I think AlphaFold proved its value from day one, by firmly establishing the knowledge (which had already been speculated) that everything you need to predict protein structures is a large enough collection of protein structures, a much larger collection of protein sequence relationships, and a collection of very savvy machine learning techniques to extract the maximum information from that to produce the most physically plausible model. It also showed that you could do all this without explicitly modelling the folding process itself, which is such a huge timesaver.