[shpongled]

We don't need an army of high school sophomores, unless they are in the lab pipetting. The expensive part of drug discovery is not the ideation phase, it is the time and labor spent running experiments and synthesizing analogues.

[falcor84]

As discussed elsewhere, Deepmind are also working on extending Alphafold to simulate biochemical pathways and then looking to tackle whole-cell simulation. It's not quite pipetting, but this sort of AI scientist would likely be paired with the simulation environment (essentially as function calling), to allow for very rapid iteration of in-silico research.

[Teever]

It sounds like you're suggesting that we need machines that mass produce things like automated pipetting machines and the robots that glue those sorts of machines together.

[11101010001100]

This exists, but does not require AI, so there is no hype.

[dekhn]

Replacing a skilled technician is remarkably challenging. Often times, when you automate this, you just end up wasting a ton of resources rather than accelerating discovery. Often, simply integrating devices from several vendors (or even one vendor) takes months.

[shpongled]

They already exist, and we use them. They are not cheap though!

[Teever]

Any idea why they're they so expensive?

[dekhn]

I've built microscopes intended to be installed inside workcells similar to what companies like Transcriptic built (https://www.transcriptic.com/). So my scope could be automated by the workcell automation components (robot arms, motors, conveyors, etc).

When I demo'd my scope (which is similar to a 3d printer, using low-cost steppers and other hobbyist-grade components) the CEO gave me feedback which was very educational. They couldn't build a system that used my style of components because a failure due to a component would bring the whole system down and require an expensive service call (along with expensive downtime for the user). Instead, their mech engineer would select extremely high quality components that had a very low probability of failure to minimize service calls and other expensive outages.

Unfortunately, the cost curve for reliability not pretty, to reduce mechanical failures to close to zero costs close to infinity dollars.

One of the reasons Google's book scanning was so scalable was their choice to build fairly simple, cheap, easy to maintain machines, and then build a lot of them, and train the scanning individuals to work with those machines quirks. Just like their clusters, they tolerate a much higher failure rate and build all sorts of engineering solutions where other groups would just buy 1 expensive device with a service contract.

[jiggawatts]

This sounds like it could be centralised, a bit like the clouds in the IT world. A low failure rate of 1-3% is comparable to servers in a rack, but if you have thousands of them, then this is just a statistic and not a servicing issue. Several hyperscalers simply leave failed nodes where they are, it’s not worth the bother to service them!

Maybe the next startup idea is biochemistry as a service, centralised to a large lab facility with hundreds of each device, maintained by a dedicated team of on-site professionals.

[dekhn]

None of the companies that proposed this concept have managed to demonstrate strong marketplace viability. A lot of discovery science remains extremely manual, artisinal, and vehemently opposed to automation.

[concordDance]

> They couldn't build a system that used my style of components because a failure due to a component would bring the whole system down and require an expensive service call

Could they not make the scope easily replaceable by the user and just supply a couple of spares?

Just thinking of how cars are complex machines but a huge variety of parts could be replaced by someone willing to spend a couple of hours learning how.

[kridsdale1]

That’s similar to how Google won in distributed systems. They used cheap PCs in shipping containers when everyone else was buying huge expensive SUN etc servers.

[dekhn]

yes, and that's the reason I went to work at google: to get access to their distributed systems and use ML to scale up biology. I never was able to join Google Research and do the work I wanted (but DeepMind went ahead and solved protein structure prediction, so, the job got done anyway).

[shpongled]

There is a big range in both automation capabilities and prices.

We have a couple automation systems that are semi-custom - the robot can handle operation of highly specific, non-standard instruments that 99.9% of labs aren't running. Systems have to handle very accurate pipetting of small volumes (microliters), moving plates to different stations, heating, shaking, tracking barcodes, dispensing and racking fresh pipette tips, etc. Different protocols/experiments and workflows can require vastly different setups.

See something like:

[1] https://www.hamiltoncompany.com/automated-liquid-handling/pl...

[2] https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-...

[Teever]

What are your thoughts on cheaper hardware like the stuff from Opentrons[0]?

I've been interested in this kind of stuff watching it from afar and now I may need to buy / build a machine that does this kind of stuff for work.

[0] https://opentrons.com/

[sizzle]

Also clinical trials

[petra]

So pharmaceutical research is largely an engineering problem, of running experiments and synthesizing molecules as fast, cheap and accurate as possible ?

[shpongled]

I wouldn't say it's an engineering problem. Biology and pharmacology are very complex with lots of curveballs, and each experiment is often different and not done enough to warrant full engineering-scale optimization (although this is sometimes the case!).

[kridsdale1]

It also seems to be a financial problem of getting VC funds to run trials to appease regulators. Even if you’ve already seen results in a lab or other country.

[ClumsyPilot]

We could have an alternative system where VC don’t need to appease regulators but must place X billion in escrow for compensation of any harm the medicine does to customers.

Regulator is not only there to protect the public, it also protects VC from responsibility

[forgetfreeman]

> VC don’t need to appease regulators

Regulations around clinical trials represent the floor of what's ethically permissible, not the ceiling. As in, these guidelines represent the absolute bare minimum required when performing drug trials to prevent gross ethical violations. Not sure what corners you think are ripe for cutting there.

[lmm]

> Regulations around clinical trials represent the floor of what's ethically permissible, not the ceiling.

Disagree. The US FDA especially is overcautious to the point of doing more harm than good - they'd rather ban hundreds of lifesaving drugs than allow one thalidomide to slip through.

[forgetfreeman]

Yeah that's not how anything works. Compounds are approved for use or not based on empirical evidence, thus the need for clinical trials. What's your level of exposure to the pharma industry?

[melagonster]

This is why FDA requires experiments before company sells any drugs.

[devmor]

This is the general problem with nearly all of this era of generative AI and why the public dislike it so much.

It is trained on human prose; human prose is primarily a representation of ideas; it synthesizes ideas.

There are very few uses for a machine to create ideas. We have a wealth of ideas and people enjoy coming up with ideas. It’s a solution built for a problem that does not exist.