Comment by lifeisloving | Hacker News Reader

Comment by lifeisloving | Hacker News Reader

Last I checked machine learning wasnt the bottleneck for drug discovery and where it fits in the research process was largely solved 5+ years ago.. Weren't there already a ton of ML based drug discovery startups that went out of business, or got aquired, not because they werent useful but because they produced more candidates than the industry could use.

I dont think I want researchers offloading their thinking to some specialty model after seeing what its done to me and my colleagues in software, it certainly isn't making anyone more competent.

It really only enables people to be able to do less novel and lower quality work faster.

OpenAI and Anthropic creating these platforms seems mostly just to create narratives around AI that its going to lead to cures for disease, which it wont, but plenty of normies assume it will cure cancer now. Also a bunch tech oligarchs who are equally convinced or 'one-shot' they're going to live forever because of language models lol.

Promises of the Fountain of Youth continue to be a time tested tools for snake oil salesmen.

This is what I've built my current startup on. We find that ideas, structures and molecules are cheap and actually making them in cells is the issue. Obviously this doesn't apply to small molecules but as groups like Anthropic move closer to Phase I I'm guessing we'll see a lot of frustration with existing CDMO providers like Wuxi or Lonza just like we see with Apple and their woes with Micron etc.

Good take - what’s your startup called? Would love to learn more

Genosys Bio - we're a London based (SF soon!) startup. We teach our models the regulatory language of DNA and use it to make synthetic 'volume controls' that let us make more of an antibody and less of a toxin.

The cool thing is since we don't change the structure/activity of an antibody it doesn't require the usual 1B + 13 years of clinical trials. Instead we cut down on the amount of times it takes and also increase production yields.

Do add me me on linkedin (Taiga Clarke) if there might be some synergy there :)

When did you check and what did you find?

For me its not a solved problem and AI/ML would still help to solve plenty Drugdiscovery in itself is such a broad area

There are still normal labs out there doing research for years on one drug alone

And biology of humans is huge and complex, not only is it not solved, its also expensive.

Even if you just not do ML drug discovery, AI can hellp with making the research faster: writing grants, finding issues, creating plans/ideas/suggestions, hepling with finding the newest research, writing code for supporting or analysis, controling microscopes and machines.

This is just futurism tech bro optimism.

Researchers in bioinfomatics that are doing the ML work are already using llms where its helpful, they dont need a specialized model. I'll bet my entire net worth and future lifetime earnings that this model will not continue to get updates a year from now.

This idea that LLMs are going to be able to help with drug discovery better than existing ML approaches is a just a lie, I dont think there's a single biotech/bioinfomatics PhD who agrees with you.

They are language models!!! Nobody said researchers wont leverage llms to do the boring stuff, but that doesnt speed up the fundamental bottleneck which are clinical studies. We already have great drug candidates, backlogs of them and having llms is not going to enable you to suddenly be able to do more clinical trials!!!

These things happen in the real world, with real constraints There is no terminal, there is no compiler, there are no proofs, money is required (loads of it).

Regardless of what Trump says, everything is not computer.

Ah yes the 'language models!!!' which still solved a milenium problem.

Drugdiscovery has also a lot of physics involved btw

And you said ML yourself. You do understand, that the current LLM explositon also means that ML in itself gets more funding and now infrastructure for ML itself got easier to get, cheaper and faster right?

And yes for sure bigger and better LLMs can help: Alone for finding potential better or more optimized experiments, there is value in an LLM having all the current research in context.

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