Even with automated experiments, the wet chemistry is slow. The kind of answer you might calculate in a second for a math problem could take many hours for the chemical reaction to occur.

Biology grad students can spend 40+ hours per week in the lab, on top of all their course work. It's not so bad though, since there's plenty of time to grade papers or work on your thesis while waiting for reactions to occur.

Yeah this, I used to sit in the lab with my girlfriend (now wife) when she was doing wet work for her pharmacology doctorate. It was slooooow. Like slower than J2EE. I could smoke a pork shoulder faster.

If she's not doing work in the lab because that part is automated, perhaps she would have time for more and better experiment design?

Many lab experiments involve waiting for a period of time, and automating the steps before and after would be both expensive and failure prone.

Have they produced statistically significant outcomes yet?

Genuine question because I don't know the answer, I've just anecdotally heard all about the promise and nothing about real world results.

tldr; No sane drug professionals would have designed a relatively simple compound (144 Atomic Mass) as a Multiple Sclerosis drug

I think human review may screw things up. If you look at dimethyl fumarate[1], no card carrying medicinal chemist would have taken this serious. No pharma exec would have risked 100s of millions of dollars for clinical research trials.

  - 1950s people notices that a wood polish cleared psoriasis. Topical formulation worked. 
  - 1990s an oral formulation was created.
  - 2000s people noticed people treated for psoriasis that had MS, had fewer MS symptoms while taking this molecule. 
  - 2017 dimethyl fumerate is approved for MS.
  [1] https://en.wikipedia.org/wiki/Dimethyl_fumarate

>The precise mechanism of action of dimethyl fumarate is not clear.

And here is the real problem. Instead of burning gigawatthours of power with GPUs, if Anthropic really wanted to help bioscience, they should just be throwing their $2 trillion at making it possible to live off of studying things like this.

We aren't in a place where a magic "Intelligence" could just look at the data and solve all our problems because we have never funded the collection of that data in the first place

We spent a lot of money on the Human Genome project and got large dividends from it but seemingly never reinvested! There's so many clear and straightforward questions we should be answering in biology that we just aren't, because nobody wants to pay for it.

LLMs aren't the right course here!