Robin: A multi-agent system for automating scientific discovery

lamename

Also on HN today "I got fooled by AI-for-science hype—here's what it taught me" https://news.ycombinator.com/item?id=44037941

hirenj

Not my subject area, but at least one other group looked at ABCA1, and judging from this abstract, it has been linked via GWAS already, and furthermore concludes it doesn’t play a role (I haven’t looked at the data though).

I don’t know, but if we were to reframe this as some software to take a hit from a GWAS, look up the small molecule inhibitor/activator for it, and then do some RNA-seq on it, I doubt it would gain any interest.

https://iovs.arvojournals.org/article.aspx?articleid=2788418

starlust2

Wouldn't the fact that another group researched ABCA1 validate that the assistant did find a reasonable topic to research?

Ultimately we want effective treatments but the goal of the assistant isn't to perfectly predict solutions. Rather it's to reduce the overall cost and time to a solution through automation.

ClaraForm

Not if (a) it misses a line of research has been refuted 1-2 years ago, (b) the experiments at recommends (RNA-Seq) are a limited resource that requires a whole lab to be setup to efficiently act based upon it, and (c) the result of the work is genetic upregulation of a gene, which could mean just about anything.

Genetic regulation can at best let us know _involvement_ of a gene, but nothing about why. Some examples of why a gene might be involved: it's a compensation mechanism (good!), it modulates the timing of the actual critical processes (discovery worthy but treatment path neutral), it is causative of a disease (treatment potential found) etc...

We don't need pipelines for faster scientific thinking ... especially if the result is experts will have to re-validate each finding. Most experts are anyway truly limited by access to models or access to materials. I certainly don't have a shortage of "good" ideas, and no machine will convince me they're wrong without doing the actual experiments. ;)

peterclary

Will we have AIs doing an increasing amount of the research, theory and even publication, with human scientists increasingly relegated to doing experiments under their direction?

lgas

If so, it won't last long. At some point AI will be able to use robots to do the experiments itself.

TechDebtDevin

lmfao

florbnit

Closed loop optimization is already a thing, and you don’t even need AI for it, just good old bayesian optimization is enough.

photochemsyn

This approach is very interesting, and one attention-catching datum is that their proposed compound, ripasudil, is now largely out-of-patent with some caveats, via Google Patents and ChatGPT 03:

> 1999 - D. Western Therapeutics Institute (DWTI) finishes the discovery screen that produced K-115 = ripasudil and files the first PCT on 4-F-isoquinoline diazepane sulfonamides. (Earliest composition-of-matter priority. A 20-year term from a 1999 JP priority date takes you to 2019 (before any extensions).

> 2005 - Kowa (the licensee) files a follow-up patent covering the use of ripasudil for lowering intra-ocular pressure. U.S. counterpart US 8 193 193 issued 2012; nominal expiry 11 July 2026. (A method-of-use patent – can block generics in the U.S. even after the base substance expires).

Scanning the vast library of out-of-patent pharmaceuticals for novel uses has great potential for curing disease and reducing human suffering, but the for-profit pipeline in academic/corporate partnerships is notoriously uninterested in such research because they want exclusive patents that justify profits well beyond a simple %-of-manufacturing cost margin. Indeed they'd probably try to make random patentable derivatives of the compound in the hope that the activity of the public domain substance was preserved and market that instead (see the Prontosil/sulfanilimide story of the 1930s, well-related in Thomas Hager's 2006 book "The Demon Under The Microscope).

I suppose the user of these tools could restrict them to in-patent compounds, but that's ludicrously anti-scientific in outlook. In general it seems the more constraints are applied, the worse the performance.

Another issue is this is a heavily studied area and the result is more incremental than novel. I'd like to see it tackle a question with much less background data - propose a novel, cheap, easily manufactured industrial catalyst for the conversion of CO2 to methanol.