“Drug repurposing for AML” lol As a person who is literally doing his PhD on AML by implementing molecular subtyping, and ex-vivo drug predictions. I find this super random. I would truly suggest our pipeline instead of random drug repurposing :) https://celvox.co/solutions/seAMLess edit: Btw we’re looking for ways to fund/commercialize our pipeline. You could contact us through the site if you’re interested!
It's almost like scientists are doing something more than a random search over language.
That a UPR inhibitor would inhibit viability of AML cell lines is not exactly a novel scientific hypothesis. They took a previously published inhibitor known to be active in other cell lines and tried it in a new one. It's a cool, undergrad-level experiment. I would be impressed if a sophomore in high school proposed it, but not a sophomore in college.
> I would be impressed if a sophomore in high school proposed it That sounds good enough for a start, considering you can massively parallelize the AI co-scientist workflow, compared to the timescale and physical scale it would take to do the same thing with human high school sophomores. And every now and then, you get something exciting and really beneficial coming from even inexperienced people, so if you can incre…
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.
The market seems excited to charge in whatever direction the weathervane has last been pointing, regardless of the real outcomes of running in that direction. Hopefully I’m wrong, but it reminds me very much of this study (I’ll quote a paraphrase) “A groundbreaking new study of over 1,000 scientists at a major U.S. materials science firm reveals a disturbing paradox: When paired with AI systems, top researchers becom…
As a dev, I have the same experience. AI chat is a massive productivity enhancer, but, when coding via prompts, I'm not able to hit the super satisfying developer flow state that I get into via normal coding. Copilot is less of a productivity boost, but also less of a flow state blocker.
Yep! I think these tools are incredibly useful, but I think they're basically changing all our jobs to be more like what product managers do, having ideas for what we want to achieve, but farming out a significant chunk of the work rather than doing it ourselves. And that's fine, I find it very hard to argue that it's a bad thing. But there's a reason that we aren't all product managers already. Programming is fun, and I do experience it as a loss to find myself doing less of it myself.
The market seems excited to charge in whatever direction the weathervane has last been pointing, regardless of the real outcomes of running in that direction. Hopefully I’m wrong, but it reminds me very much of this study (I’ll quote a paraphrase) “A groundbreaking new study of over 1,000 scientists at a major U.S. materials science firm reveals a disturbing paradox: When paired with AI systems, top researchers becom…
I'm a bit skeptical of this study given how it is unpublished, from a (fairly junior) single author and all of the underlying details of the subject are redacted. Is there any information anywhere about what this company in the study was actually doing? (the description in the article are very vague -- basically something to do with materials)
> I would be impressed if a sophomore in high school proposed it That sounds good enough for a start, considering you can massively parallelize the AI co-scientist workflow, compared to the timescale and physical scale it would take to do the same thing with human high school sophomores. And every now and then, you get something exciting and really beneficial coming from even inexperienced people, so if you can incre…
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.
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.
Tbh I don’t see why I would use this. I don’t need an ai to connect across ideas or come up with new hypothesis. I need it to write lots of data pipeline code to take data that is organized by project, each in a unique way, each with its own set of multimodal data plus metadata all stored in long form documents with no regular formatting, and normalize it all into a giant database. I need it to write and test a data…
I think you're just not the target audience. If AI can come up with some good ideas and then split it into tasks some of them an undergrad can do - it can speed up the global research speed by involving more people in useful science
In science, having ideas is not the limiting factor. They're just automating the wrong thing. I want to have ideas and ask the machine to test for me, not the other way around.
I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…
Not that I don't think there's a lot of potential in this approach, but the leukemia example seemed at least poorly-worded, "the suggested drugs inhibit tumor viability" reads oddly given that blood cancers don't form tumors?
Lots of blood cancers form solid tumors (e.g., in lymph nodes)
Tbh I don’t see why I would use this. I don’t need an ai to connect across ideas or come up with new hypothesis. I need it to write lots of data pipeline code to take data that is organized by project, each in a unique way, each with its own set of multimodal data plus metadata all stored in long form documents with no regular formatting, and normalize it all into a giant database. I need it to write and test a data…
> I don’t need an ai to connect across ideas or come up with new hypothesis. This feels like hubris to me. The idea here isn't to assist you with menial tasks, the idea is to give you an AI generalist that might ne able to alert you to things outside of your field that may be related to your work. It's not going to reduce your workload, in fact, it'll probably increase it but the result should be better science. I ha…
> This feels like hubris to me.
No, any scientist has hundreds of ideas they would like to test. It's just part of the job. The hard thing is to do the rigorous testing itself.
So I'm a biomedical scientist (in training I suppose...I'm in my 3rd year of a Genetics PhD) and I have seen this trend a couple of times now where AI developers tout that AI will accelerate biomedical discovery through a very specific argument that AI will be smarter and generate better hypotheses than humans. For example in this Google essay they make the claim that CRISPR was a transdisciplinary endeavor, "which c…
It's pretty painful watching CS try to turn biology into an engineering problem.
It's generally very easy to marginally move the needle in drug discovery. It's very hard to move the needle enough to justify the cost.
What is challenging is culling ideas, and having enough SNR in your readouts to really trust them.
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.
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.
This exists, but does not require AI, so there is no hype.