5 ms·

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

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.

Teever 2y ago
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 2y ago
  This exists, but does not require AI, so there is no hype.
- shpongled 2y ago
  They already exist, and we use them. They are not cheap though!
  - Teever 2y ago
    Any idea why they're they so expensive?
    - shpongled 2y ago
      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/platforms/microlab-vantage-liquid-handling-system https://www.hamiltoncompany.com/automated-liquid-handling/pl... [2] https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-yfm8h01 https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-...
      - Teever 2y ago
        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/ https://opentrons.com/
    - dekhn 2y ago
      I've built microscopes intended to be installed inside workcells similar to what companies like Transcriptic built (https://www.transcriptic.com/ 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.
      - kridsdale1 2y ago
        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 2y ago
        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 2y ago
        They really didn't solve it. AF works great for proteins that have a homologous protein with a crystal structure. It is absolutely useless for proteins with no published structure to use as a template - e.g. many of the undrugged cancer targets in existence.
- dekhn 2y ago
  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.
petra 2y ago
So pharmaceutical research is largely an engineering problem, of running experiments and synthesizing molecules as fast, cheap and accurate as possible ?
- shpongled 2y ago
  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 2y ago
  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 2y ago
    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
    - melagonster 2y ago
      This is why FDA requires experiments before company sells any drugs.
    - forgetfreeman 2y ago
      > 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.
devmor 2y ago
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.
falcor84 2y ago
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.
sizzle 2y ago
Also clinical trials

Teever 2y ago

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 2y ago
This exists, but does not require AI, so there is no hype.
shpongled 2y ago
They already exist, and we use them. They are not cheap though!
- Teever 2y ago
  Any idea why they're they so expensive?
  - shpongled 2y ago
    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/platforms/microlab-vantage-liquid-handling-system https://www.hamiltoncompany.com/automated-liquid-handling/pl... [2] https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-yfm8h01 https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-...
    - Teever 2y ago
      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/ https://opentrons.com/
  - dekhn 2y ago
    I've built microscopes intended to be installed inside workcells similar to what companies like Transcriptic built (https://www.transcriptic.com/ 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.
    - kridsdale1 2y ago
      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 2y ago
        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 2y ago
        They really didn't solve it. AF works great for proteins that have a homologous protein with a crystal structure. It is absolutely useless for proteins with no published structure to use as a template - e.g. many of the undrugged cancer targets in existence.
dekhn 2y ago
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.

petra 2y ago

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

shpongled 2y ago
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 2y ago
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 2y ago
  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
  - melagonster 2y ago
    This is why FDA requires experiments before company sells any drugs.
  - forgetfreeman 2y ago
    > 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.

devmor 2y ago

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.

falcor84 2y ago

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.

sizzle 2y ago

Also clinical trials