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.
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.
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.
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)
> What made this prediction so exciting was that it was a novel idea. Although CK2 has been implicated in many cellular functions, including as a modulator of the immune system, inhibiting CK2 via silmitasertib has not been reported in the literature to explicitly enhance MHC-I expression or antigen presentation. This highlights that the model was generating a new, testable hypothesis, and not just repeating known facts.
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.
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.
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.
I am concerned about this kind of technology being used to circumvent traditional safeguards and international agreements that prevent the development of biological weapons.
Well you might be pleased to know that there are large safety teams working at all frontier model companies worried about the same thing! You could even apply if you have related skills.
> the oversight of more estabilished international agreements and safeguards?
“Unlike the chemical or nuclear weapons regimes, the [Biological Weapons Convention] lacks both a system to verify states' compliance with the treaty and a separate international organization to support the convention's effective implementation” [1].
I mean.. they work within the legal frameworks of very large corporations with nation state engagement. It's not like they're autonomous anonymous DAOs
Hi! I work directly on these teams as a model builder and have talked to my colleagues are the other labs well.
All our orgs have openings and if you also could consider working for organizations such as the UK AISI team and other independent organizations that are assessing these models. It's a critical field and there is a need for motivated folks.
Seems like no matter how positive the headline about the technology is, there is invariably someone in the comments pointing out a worst case hypothetical. Is there a name for this phenomenon?
Not believing everything you read on the internet? Being jaded from constant fluff and lies? Not having gell-mann amnesia?
I get your sentiment of "why you gotta bring down this good thing" but the answer to your actual question is battle scars from the constant barrage of hostile lies and whitewashing we are subject to. It's kind of absurd (and mildly irresponsible) to think "THIS time will be the time things only go well and nobody uses the new thing for something I don't want".
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.
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.
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.
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)
> What made this prediction so exciting was that it was a novel idea. Although CK2 has been implicated in many cellular functions, including as a modulator of the immune system, inhibiting CK2 via silmitasertib has not been reported in the literature to explicitly enhance MHC-I expression or antigen presentation. This highlights that the model was generating a new, testable hypothesis, and not just repeating known facts.
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.
If someone discovers anything, it does not change anything if someone else could have discovered it theoretically as well?
This is so awesome. Hoping those in the biology field can comment on the significance.
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.
It is awesome.
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.
Meanwhile OpenAI going into the porn business
If you can have porn without the human trafficking and exploitation associated with the porn industry, that's a big win too
Their research is pivoting to STDs
Both is equally important, just in another dimension.
Let's go !!!
Hell yeah
I am concerned about this kind of technology being used to circumvent traditional safeguards and international agreements that prevent the development of biological weapons.
Well you might be pleased to know that there are large safety teams working at all frontier model companies worried about the same thing! You could even apply if you have related skills.
I thought openAI gave up on safety when Anthropic splintered off as well as when they engaged ScaleAI to traumatize people for RLHF?
Or Google when they fired Timnit?
The GPT5 system card is 59 pages. Pages 5 to 56 address safety in various forms.
https://cdn.openai.com/gpt-5-system-card.pdf
Are these safety teams subject to the oversight of more estabilished international agreements and safeguards?
> the oversight of more estabilished international agreements and safeguards?
“Unlike the chemical or nuclear weapons regimes, the [Biological Weapons Convention] lacks both a system to verify states' compliance with the treaty and a separate international organization to support the convention's effective implementation” [1].
[1] https://en.wikipedia.org/wiki/Biological_Weapons_Convention
I mean.. they work within the legal frameworks of very large corporations with nation state engagement. It's not like they're autonomous anonymous DAOs
Hi! I work directly on these teams as a model builder and have talked to my colleagues are the other labs well.
All our orgs have openings and if you also could consider working for organizations such as the UK AISI team and other independent organizations that are assessing these models. It's a critical field and there is a need for motivated folks.
That does not answer my question.
Seems like no matter how positive the headline about the technology is, there is invariably someone in the comments pointing out a worst case hypothetical. Is there a name for this phenomenon?
Rational discourse? Not working for a marketing team? Realism?
Performative cynicism?
Not believing everything you read on the internet? Being jaded from constant fluff and lies? Not having gell-mann amnesia?
I get your sentiment of "why you gotta bring down this good thing" but the answer to your actual question is battle scars from the constant barrage of hostile lies and whitewashing we are subject to. It's kind of absurd (and mildly irresponsible) to think "THIS time will be the time things only go well and nobody uses the new thing for something I don't want".
Pessimism?