Comment by dekhn
3 hours ago
Yes, we explicitly avoided any kind of high concurrency/parallelism. That was an intentional decision, although note we did have a global torrent network across all the workers, whose topology (node interactions) formed an extremely complex graph.
I used to work in the MPI world on supercomputer jobs and it was miserable. I also worked on other high concurrency systems at Google (helped run the Ads DB, did training and engineering of Sibyl). I continue to insist these systems are incredibly simple compared to even the most basic human cells interactin g.
Well, we have seen AlphaFold solving a decades old protein folding problem. There's no reason for me to believe, with all the AI advancements I've seen in the meanwhile, that we will not solve other problems in this field too.
AlphaFold didn't solve a decades old protein folding problem. They substantially improved protein structure prediction but with important limits.
THe primary bottleneck for a drug is getting it through clinical trials. No matter how much you speed up lead detection, development, and in vivo studies, the bottleneck will still be clinical trials. Now, if we could make lead detection better at generating molecules that get through clinical trials, that's great- but it would take decades to know for sure. If we could make development less likely to produce toxic molecules that trigger the immune system, sure that's great- but again, we're talking decades before we know if it worked.
Attempting to improve clinical trials- well, those are already fairly well designed and run, but it's likely you could make improvements there too that increased efficacy. But again, you're still just dealing with the FDA, filing tons of paperwork, and tons of people looking at the paperwork. Unless you fix those systematic issues, we're not going to accelerate drug approvals.