Accelerating scientific breakthroughs with an AI co-scientist

Post by forgetfreeman » Fri, Feb 21, 2025, 4:14 PM UTC

lmm wrote: post #176

forgetfreeman wrote: post #172

Earlier quoted context omitted.

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?

> Compounds are approved for use or not based on empirical evidence, thus the need for clinical trials. But off-label use is legal, so it's ok to use a drug that's safe but not proven effective (to the FDA's high standards) for that ailment... but only if it's been proven effective for some other random ailment. That makes no sense. > What's your level of exposure to the pharma industry? Just an interested outsider w…

I strongly encourage you to take a half hour and have a look at what goes into preclinical testing and the phases of official trials. An understanding of the data gathered during this process should clear up some of your confusion around safety and efficacy of off-label uses, which parenthetically pharma companies are strictly regulated against encouraging in any way.

Post by killjoywashere » Mon, Feb 24, 2025, 1:19 AM UTC

dekhn wrote: post #80

crypto420 wrote: post #11

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…

So, I've been reading Google research papers for decades now and also worked there for a decade and wrote a few papers of my own. When google publishes papers, they tend to juice the results significance (google is not the only group that does this, but they are pretty egregious). You need to be skilled in the field of the paper to be able to pare away the exceptional claims. A really good example is https://spectrum…

I have worked with Google teams as well, and they taught me a fair bit about how to be rigorously skeptical. It takes domain knowledge, statistical knowledge, data, time and the computational resources to challenge them. I've done it, but it took real resources.

That said, it's a useful exercise to figure out the plan of attack. My experience is the "juice" was mainly in "easy true negative" subclasses. They weren't oversampled, but the human brain wouldn't even consider most of that data. Once you ablate those subclasses from the dataset, (which takes a lot of additional labelling effort), you can start challenging their assertions. But it's hard.

And that said I also review a number of articles in that domain, and I haven't seen a group with stronger datasets overall.

Re: Accelerating scientific breakthroughs with an AI co-scientist

Re: Accelerating scientific breakthroughs with an AI co-scientist