I am concerned about this kind of technology being used to circumvent traditional safeguards and international agreements that prevent the development of biological weapons.
We’ve just had a virus - specifically engineered to be highly infectious for humans - escaping the lab (which was running very lax safety level - BSL2 instead of required BSL4) and killing millions and shutting down half the globe. So I’m wondering what safeguards and prevention you’re talking about :)
For all the hate that Google (rightly) gets for some of their work in other domains, I appreciate that they continue to put major resources behind using AI to try and save lives in medicine and autonomous driving.
Easy to take for granted, but their peer companies are not doing this type of long term investment.
Someone has to do it. Big pharma has a lot of money and if AI can reduce their costs in human resources, they will be willing to put some of their profits aside to further the research in AI space.
Money wells are drying up across the trch Industry and ai companies will have to look for funds from adjacent industries like biotech and medicine.
I think it's important that people know this. Despite what the other AI companies claim or put out as occasional PR, they have absolutely no real interest (through internal work or funding external researchers) in using AI to benefit science and humanity as a whole. They just want their digital god. As such, there is simply not enough funding for AI research with scientific applications. Consequently, many people in machine learning are not working in scientific applications, even though they really want to.
But what I’ll say is, ideally they would demonstrate whether this model can perform any better than simple linear models for predicting gene expression interactions.
We’ve seen that some of the single cell “foundation” models aren’t actually the best at in silico perturbation modeling. Simple linear models can outperform them.
So this article makes me wonder: if we take this dataset they’ve acquired, and run very standard single cell RNA seq analyses (including pathway analyses), would this published association pop out?
My guess is that yes… it would. You’d just need the right scientist, right computational biologist, and right question.
However, I don’t say this to discredit the work in TFA. We are still in the early days of scSeq foundation models, and I am excited about their potential.
Cellular level computational simulation existed a very long time and it's more impressive by the day because of large collections of experimental datasets available.
However to infer or predict celular acitivities you need a ton of domain knowledge and experties about particular cell types, biological processes and specific environments. Typically the successful ones are human curated and validated (e.g large interaction networks based on literature).
In cancer it's even more unpredictable because of the lack of good (experimental) models, in-vivo or in-vitro, representing what actually happens the clinically and biologically underneath. Given the single cell resolution, its uncertainty will also amplify because of how heterogeneous inter- and intra- tumours are.
Having said that, a foundation model is definitely the future for futher development. But with all of these things, the bigger the model, the harder the validation process.
Remarkably some claim AI has now discovered a new drug candidate on its own. Reading the prep-print (https://www.biorxiv.org/content/10.1101/2025.04.14.648850v2....), it appears the model was targeted to just a very specific task and without evaluating other models on the same task. I know nothing about gens, and I can see that is an important advance. However, seems a bit headline grabbing when claiming victory for one model without comparing against others using the same process.
From what I understand, the model was used to broaden a search that was already conducted by humans. It's not like the model has devised new knowledge. Kind of a low hanging fruit. But question is: how many of these can be reaped ? Hopefully a lot!
("low hanging fruit", well, not the right way to put it, Google's model are not exactly dumb technology)
Other potential cancer treatment methods that 2.5pro - a different model than is referenced in the article - has confirmed as potentially viable when prompted by an amateur cancer researcher:
- CPMV; Cow-Pea Mosaic Virus (is a plant virus that doesn't infect humans but causes an (IFN-1 (IFN-alpha and a lot of IFN-beta)) anti-cancer response in humans. Cow Pea consumption probably used to be even more prevalent in humans before modern agriculture; cow peas may have been treating cancer in humans for thousands of years at least.)
I emailed these potential new treatments to various researchers with a fair disclaimer; but IDK whether anything has been invested in developing a treatment derived from or informed by knowledge of the relevant pathways affected by EPS3.9 or CPMV.
There are RNA and mRNA cancer vaccines in development.
Without a capsid, RNA is destroyed before arrival. So RNA vaccines are usually administered intramuscularly.
AFAIU, as a general bioengineering platform, CPMV Cow-Pea Mosaic Virus could also be used like a capsid to package for example an RNA cancer vaccine.
AFAIU, CSC3.9 (which produces the "potent anti-cancer" EPS3.9 marine spongiibacter polysaccharide) requires deep sea pressure; but it's probably possible to bioengineer an alternative to CSC3.9 which produces EPS3.9 in conditions closer to ambient temp and pressure?
> Would there be advantages to (CPMV + EPS3.9) + (CPMVprime + mRNA)? (for cancer treatment)
Sometimes it feels like Google are so far ahead in AI but all we get to see are mediocre LLMs from Open AI. Like they're not sharing the really good stuff with everyone.
It doesn't compensate for all the other bad stuff google's doing.
Long gone are the times were I looked up to google as a champion of good technologies. When I read this I'm just sad such en important step of humankind's technological future is in the hand of evil(-ishbat least) companies
17 comments
[ 3.1 ms ] story [ 46.3 ms ] threadEasy to take for granted, but their peer companies are not doing this type of long term investment.
Money wells are drying up across the trch Industry and ai companies will have to look for funds from adjacent industries like biotech and medicine.
But what I’ll say is, ideally they would demonstrate whether this model can perform any better than simple linear models for predicting gene expression interactions.
We’ve seen that some of the single cell “foundation” models aren’t actually the best at in silico perturbation modeling. Simple linear models can outperform them.
So this article makes me wonder: if we take this dataset they’ve acquired, and run very standard single cell RNA seq analyses (including pathway analyses), would this published association pop out?
My guess is that yes… it would. You’d just need the right scientist, right computational biologist, and right question.
However, I don’t say this to discredit the work in TFA. We are still in the early days of scSeq foundation models, and I am excited about their potential.
However to infer or predict celular acitivities you need a ton of domain knowledge and experties about particular cell types, biological processes and specific environments. Typically the successful ones are human curated and validated (e.g large interaction networks based on literature).
In cancer it's even more unpredictable because of the lack of good (experimental) models, in-vivo or in-vitro, representing what actually happens the clinically and biologically underneath. Given the single cell resolution, its uncertainty will also amplify because of how heterogeneous inter- and intra- tumours are.
Having said that, a foundation model is definitely the future for futher development. But with all of these things, the bigger the model, the harder the validation process.
("low hanging fruit", well, not the right way to put it, Google's model are not exactly dumb technology)
- EPS3.9: Polysaccharide (deep sea bacterium sugar, fermentable, induces IFN-1) causes Pyroptosis causes IFN-1 causes Epitope Spreading (which is an amplifying effect) causes anti-cancer response.
- CPMV; Cow-Pea Mosaic Virus (is a plant virus that doesn't infect humans but causes an (IFN-1 (IFN-alpha and a lot of IFN-beta)) anti-cancer response in humans. Cow Pea consumption probably used to be even more prevalent in humans before modern agriculture; cow peas may have been treating cancer in humans for thousands of years at least.)
I emailed these potential new treatments to various researchers with a fair disclaimer; but IDK whether anything has been invested in developing a treatment derived from or informed by knowledge of the relevant pathways affected by EPS3.9 or CPMV.
There are RNA and mRNA cancer vaccines in development.
Without a capsid, RNA is destroyed before arrival. So RNA vaccines are usually administered intramuscularly.
AFAIU, as a general bioengineering platform, CPMV Cow-Pea Mosaic Virus could also be used like a capsid to package for example an RNA cancer vaccine.
AFAIU, CSC3.9 (which produces the "potent anti-cancer" EPS3.9 marine spongiibacter polysaccharide) requires deep sea pressure; but it's probably possible to bioengineer an alternative to CSC3.9 which produces EPS3.9 in conditions closer to ambient temp and pressure?
> Would there be advantages to (CPMV + EPS3.9) + (CPMVprime + mRNA)? (for cancer treatment)