dekhn
Joined 14 years ago
Joined 14 years ago
As a side note: I take prozac and it's far better than paxil (for me and many others). Has been transformative in my life.
I have never in my life felt worse than when I was getting off paxil. Beyond the head zaps, it pervaded my intellectual with depression and anxiety.
Only after a few "head zaps" and other papers got published did the psych folks even listen to me about it, and then they were just sympathetic and tried to find me something else.
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.
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.
At peak, we had 1-3 million xeon cores running our tasks (protein folding, protein design, drug docking), 24/7. This was before GPUs were really a thing. Each mini-task was basically a rosetta or gromacs or autodock binary running on local inputs and writing local outputs for ~1 hour using ~1GB RAM and no network. Input and output was via colossus, or distributed through a global torrent network (literally all 1-3 million cores formed a single data transfer group, with data passed between clusters at low probability).
I built most of the code of the system (which depended on Google infra) including the scheduler and the machine worker, and also ran the system as on-call "SRE" for 3 years. During that time, we nearly almost didn't take down the Google global network, and had small latency impacts on many services. Managing to run the system for so long without antagonizing ads or search or youtube was a major challenge.
I would say the complexity of the system I built (considering everything down to the OS) was roughly the level of a few human cells in a plate, in terms of interacting components and the complexity of their interactions.
Here are some of the publications that came from running exacycle (we provided strong evidence that using more compute improves outcomes in molecular simulations)
https://pubmed.ncbi.nlm.nih.gov/24345941/ https://pubmed.ncbi.nlm.nih.gov/24265211/
https://www.sfgate.com/bayarea/article/Tanker-fire-destroys-...
(Also, the rebuilding was epic, the only time I've seen the state and a contractor move quickly: https://longreads.com/2024/07/15/rebuilding-the-maze/ )
I distinctly recall that Robotech had a cross-dressing character well ahead of its time (for american audiences).