Comment by jrowen | Hacker News Reader

Comment by jrowen | Hacker News Reader

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It's true though. It's not like math or software where you can do it isolation and work things out logically "on paper." I don't think the solution will be "put a bunch of agents on it and let them figure it out." The tools to model and observe what's going on in full detail don't exist. Far from it. You can't really optimize the trial and error feedback loop time.

I think it will look more like Claude just helping every expert along the way do their job better. Analyzing and synthesizing data to make better experimental suggestions and design new equipment (or totally new experiments based on unconceived-of apparatuses?). Managing the flow of information and building bespoke knowledge bases and tools. The more it is integrated into all the systems and researchers involved the faster it can put things together.

Although, on second thought, maybe there even is a near future where it takes our models and understanding and just galaxybrains a better model. Has anyone tried feeding it some collection of datasets and saying "can you make a more detailed model of a cell?"

> You can't really optimize the trial and error feedback loop time.

Isn't this exactly what startups in this area are doing - automated experiments connected to AI. Of course at some stage things will "graduate" to human review but it still seems to have a lot of potential.

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.

Or in other words: the bulk of the work is already automated! The conditions have been set up and the reactants are doing the work of reacting. There’s nothing for the experimenter to do but wait.

The reaction can’t be sped up, its rate is wholly constrained by the nature of the compounds involved and the physical parameters of the experiment, which may be very narrow to avoid unwanted products or even thermal runaway which could cause a total failure of the experiment or even a dangerous release of poisonous or explosive materials.

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!

> You can't really optimize the trial and error feedback loop time.

They can put it onto robots / automatic labs, so this step is easily solvable. Humans in a modern lab are largely unnecessary. Just look at China.

The problem here is that Anthropic claims to have solved this all. It is time that they actually prove their claim. Right now it reads like an advertisement to inflate the stock of Anthropic.

> Humans in a modern lab are largely unnecessary. Just look at China.

Care to elaborate, explain?

> They can put it onto robots / automatic labs, so this step is easily solvable. Humans in a modern lab are largely unnecessary. Just look at China.

You can automate the setup and mechanical process. You could increase the physical capacity for parallel tests. You could increase the rate of acquiring raw materials.

But how much you cut down the time depends on how much of the trial-and-error feedback loop depends on feedback and iteration vs cast a wider initial net.

(One of the interesting things to me about the dominant software development agent pattern across all of Claude, Codex, and Cursor is that the approach is still extremely local and iterative, human-mimicking, rather than big-brain-one-shotting a novel or perfectly-fit-for-purpose software design. The performance of the agents on the initial "jump to conclusions" step does not appear superhuman in the same way that the speed-of-iteration is clearly superhuman.)

> Humans in a modern lab are largely unnecessary.

That is utterly untrue. It stems from the notion that biology only needs big data for us to solve all its mysteries.

Beyond that, while automated labs for cell biology are plausible and active, and the same fit organoid biology, the same really isn’t true at organism level. Labs already are artificial environments that make organisms have odd phenotypes, and odd interactions which bias the data away from what you’d find in the natural world.

You add in robotic manipulation, and you add a new confound.

Drones in the wild are an obvious counterbalance to this, though even in that scenario, the drone can be a confound unless well hidden.

In the end though, this kind of systemic breakdown of biology, while much needed raw material to improve our theories, won’t solve the whole of biology. Human hands and human minds will be needed for a good long while.

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