r/science • u/mvea Professor | Medicine • 1d ago
Medicine MIT used AI to develop new recipe for mRNA vaccines so they no longer need freezers and can sit at room temp for a full year or survive about 100F heat for two months which could finally let these vaccines reach poor countries without cold storage and even work as easy skin patches instead of shots.
https://news.mit.edu/2026/new-formulation-helps-rna-vaccines-withstand-high-temperatures-09283.6k
u/imtalkintou 1d ago
What AI should be used for.
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u/Crystalas 1d ago edited 1d ago
Also another example of why having AI being such a blanket term is so annoying. Cannot even compare LLMs, glorified chatbots, to tools like this they just so different in the way they built and function. But because that is the term most people use it furthers misinformation, and that before getting into how AI is presented in fiction shaping people's understanding of it in reality.
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u/Uncynical_Diogenes 1d ago
Drives me up the wall.
This is a really cool project, but it’s what machine learning systems have been doing for years before anybody heard about ChatGPT.
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u/Crystalas 1d ago
Machine learning breakthroughs like this are not even new, they been having amazing results for like 15+ years. Although they definitely been improving year on year, one of the few things that gives me hope.
There even used to be various programs could use to "donate" your PC's idle time to running them, like "folding@home". They also used to at times be run off of linked together playstations due to cost and their design just being optimal for the job, Sony did NOT like that.
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u/worldspawn00 1d ago
I was working in a university research program in 2010-12 on training enzyme structure prediction machine learning models, it's damn cool stuff, and nothing to do with LLMs or the current era of what people are calling AI. We really should be using different terms for these.
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u/donut-reply 22h ago
Yes, I hate when people defend further crazy bubble investment in LLM training by saying it's worth it if it helps develop new drugs etc. 2 very different things.
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u/asyork 11h ago
At best, LLMs could function as the human interface module for a real AI (as the term was understood decades ago through science fiction) that is reliably useful. They are neat, if you ignore the fact that the amount of money and effort put into them could have made countless neat things that don't destroy the world. We keep trying to shoehorn the glorified chatbots into many roles that would have been better handled by specialized machine learning systems rather than LLMs, but the billionaires don't want to have to build countless specialized systems for each task. Or worse, have it become something companies have to hire experienced workers to do in house for their specific needs, rather than being a monolithic product everyone needs to buy from one of three or so companies that can afford to write their own laws to protect themselves from more competition.
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u/obsequiousaardvark 1d ago
Don't worry they renamed it Super Intelligence. I wish that I was joking.
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u/Rogarth0 23h ago
"They" didn't do that, a certain orange thing did. The correct thing to do is just ignore it.
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u/jezuschryzt 21h ago
"The name has been officially changed by the biggest, the smartest, the greatest people anywhere in the world at this" *facepalm*
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u/dustyjuicebox 1d ago
They're both built around neural networks though? While the details can differ, the general technology improvements to LLMs are also applied to scientific models.
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u/klockee 23h ago
Humans and plants are both built from cells and yet we differentiate those pretty heavily
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u/Uncynical_Diogenes 23h ago
Yupp. Common ancestor? Check. Similar components? Sure.
Very different architecture and teleology.
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u/BoogieOrBogey 1d ago
My understanding is that the LLMs for research tools are trained different than models like Claude or ChatGPT, including using very different training data. So it's the same base technology, but the products are very different. Similar to how gunpowder is used in firearms, artillery, and dynamite but those products are clearly different things.
If I'm wrong here, then I invite someone else to provide a more accurate answer. The research and science of LLMs are murky to me.
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u/AwesomePantsAP 1d ago
LLMs just plain aren’t used here. Transformer architectures sure, but these models never even touch language (a pretty key part of large language models).
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u/BoogieOrBogey 23h ago
Ah okay, so the base is more of Machine Learning than the LLM layer. Great example for how the umbrella term "AI" does a bad job actually describing what's going on.
Actually looking at the paper, and not the article, gives this tool a unique name as well.
Here we introduce Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization (AGENT), an artificial intelligence (AI)-driven framework that couples high-throughput experimentation with Bayesian optimization to identify thermostable mRNA−LNP formulations.
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u/metallicrooster 16h ago
I usually call ChatGpt and programs like it chatbots for that reason.
Are today’s chatbots better than the ones on AIM in the early 2000s? Of course they are. But they’re still chat bots at the end of the day. They are not programmed to provide correct information. They are programmed to spit out plausible looking text. They are not the same as the programs that you referred to.
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u/round-earth-theory 1d ago
The majority of those improvements have come from more and more processing power. The algorithms aren't fundamentally changing, but the data volumes that can be processed are immense compared to it's earliest iterations.
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u/ArcaneNine 23h ago
Not quite, self-attention/transformer architecture was a pivotal breakthrough that allowed models to capture the relevant data in a way that was specific to its context. Once that was established, it unlocked the ability to throw tons and tons of raw data at a model and actually have it improve its performance instead of generalizing poorly. The AI boom and investment races happened basically after the adoption of transformer self-attention architecture.
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u/JohnLockeJaw 1d ago
This isn't entirely true. Most of our deep neural networks had really plateued and adding more layers wasn't helping. Check out the history of AlexNet for more details.
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u/Crystalas 1d ago
Speaking of iterations, that another factor. Iterating over the same data and on the results of prior ones to further refine with new data and variables, it a feedback loop.
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u/cat_at_the_keyboard 1d ago
I used to run folding@home on my garbage pc about 20 years ago. I wish I remembered the specs but I wouldn't be surprised if my current pc is 10x better at least
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u/calicosiside 22h ago
If your pc was garbage in 2006 it's probably closer to x100 times more processing power honestly, although how much of that is taken up by the OS is a different question
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u/Swarna_Keanu 1d ago
That is the one positive I see here: LLMs made producing processors that specialise on AI needs (not just LLMs) commercially sensible - and AI in general will benefit from that.
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u/aVarangian 1d ago
you can still do folding@home and other such projects
useful during cold winters
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u/Bhraal 18h ago
There even used to be various programs could use to "donate" your PC's idle time to running them, like "folding@home"
And then AlphaFold came along and more than 1,000x the number of discovered proteins in just one year. Distributed computing has it's uses where the workload can consistently be broken down into relatively small tasks, but I suspect dedicated neural networks have taken any chance it had for being a significant part of groundbreaking discoveries.
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u/Crystalas 18h ago
Crypto-mining probably didn't help either.
Those with PCs strong enough to be worth doing it on, at least for awhile, were also strong enough to turn a profit mining instead of increasing electric bill & hardware wear for charity. Some would still do it but I imagine the number of users doing it were small enough that any stopping or choosing not to give it a try could be noticeable.
And then later the mess with hardware shortages and miners emptying inventories on various components thus directly competeing with those who might have used it used this way.
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u/bunker_man 1d ago
You can absolutely compare them, since a lot of these tools have the same basic architecture, and the ones people see as wierd novelties are tied to tech that is actually more useful. Hence how midjourney can do stuff like pivot into medical tech.
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u/galactictock 1d ago
I completely agree about overuse of the term “AI” muddying the waters. But calling LLMs “glorified chatbots” is a gross misrepresentation. LLMs have more limitations than some would have you believe, but they are orders of magnitude more capable, sophisticated, and useful than pre-LLM chatbots.
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u/ackermann 1d ago edited 1d ago
Yes. They’re not as powerful as some want you to believe… but LLMs have made some discoveries.
Here’s an interesting one in biology/medicine:
https://www.reddit.com/r/singularity/s/DDGNtwbMjLAlong with the rapidly growing number of unsolved math problems it’s tackled.
The days of “it can’t reason, all it does is regurgitate its training data” are mostly gone, given it’s solving unsolved problems.
(Now the goalposts seem to have moved to “but it’s just doing brute force, monkeys on a keyboard”)26
u/SemaphoreBingo 1d ago
Here’s an interesting one in biology/medicine
Didn't that end up being something that they scooped from a pre-preprint that the original authors had uploaded?
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u/galactictock 1d ago
Yes, there has been no shortage of research on this. It’s frustrating that this still needs to be said, especially in this sub. Research has demonstrated that LLMs are capable of abstraction and many forms of reasoning and can be leveraged for finding novel solutions to problems, as highlighted by your example.
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u/MachinationMachine 1d ago
It's not true reasoning unless it's made in the Reasoning Region of France.
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u/ubernutie 1d ago
Now the goalposts seem to have moved
It will keep happening. It's ok, that's how these things work.
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u/bunker_man 1d ago
And then a few years from now when its commonly accepted the people who claimed it would never be useful will all deny they held these stances.
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u/ubernutie 1d ago
Definitely.
And even worse, it will all have been so obvious in hindsight for totally everyone you talk to.
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u/shadowndacorner 1d ago
The thing people miss when regurgitating the idea that LLMs can't solve complex problems is that almost any complex problem is simply a coherent aggregation of simple problems. If you have something that can solve all of those simple problems and you have a skilled human (or these days even a high end local model) able to break the complex problem down... you have a system that can solve complex problems, by solving simple problems.
Very rarely is complexity atomic.
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u/ProofJournalist 1d ago
The goalposts will keep moving. That's how rigprpus scientific validation works
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u/katarh 1d ago
The issue we have is that some of those math problems are being solved by improperly accessing the data of other researchers, without their permission.
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u/galactictock 1d ago
LLMs are highly capable, but the frontier labs absolutely cannot be trusted with sensitive data.
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u/ackermann 1d ago edited 1d ago
That may be true of the millennium problem (still controversial), where it may have got a headstart, a hint at the right direction from a researcher who was close.
But that researcher hadn’t fully solved it. No one had before the AI did.And there are plenty of problems like Erdos #1196, and also the Unit Distance Problem, where the LLM’s solution was pretty novel
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u/NOTHING_gets_by_me 1d ago
Buckmaster (one of the researchers you're talking about) said they might have did that, OAI denies it of course, so that's unsettled. More importantly for this discussion, Buckmaster also said that the mechanism the agents found and used to make the leap from his work (on Euler equations) to the millennium prize solution is something he hadn't considered at all, and may never have, despite it being pretty much being very directly related to his fields of work for the last decade +
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u/WWJLPD 22h ago
Somewhere in corporate America, an upper middle manager has seen the headline of this article and has already sent it as part of an AI slop-infused pep talk message to their team in which they glorify LLMs the way bronze age cultures praised their local deities, proclaim AI to be the inevitable future, and ended with a call to action that implies that their team should be getting results of a similar magnitude with the company’s Claude subscription
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u/lumpboysupreme 1d ago edited 22h ago
These ARE LLM’s, just ones trained to focus on something else. But they’re the same underlying tech. They’re built on BERT.
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u/OuterOne 21h ago
Where does or mention BERT? I can't see it in the article or paper abstract.
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u/Blopslopper 1d ago
Except it's the same tech. Large Language Models are the upgrade powering all this.
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u/nishinoran 23h ago
If I'm being pedantic I think I might say that transformer models, the underlying architecture of LLMs, are the upgrade, on top of the hardware availability that's being funded by LLMs.
LLMs also provided the proof that scaling transformer models can give amazing results, and I wouldn't be surprised if concepts like simulated reasoning are being applied to other fields as well (although I'm not 100% sure how that looks without language).
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u/lectric_7166 18h ago
Being an ideologue must be so tiring. You spend forever railing against X technology, then X starts doing undeniably amazing things, so you're left whining about how only bad things should be called X, not the good things, to help with your circular reasoning.
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u/keelem 1d ago
Cannot even compare LLMs, glorified chatbots
This is like a conservative saying "vaccines killed more people than they saved". Completely insane take that shows how successful foreign propaganda on reddit is.
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u/bunker_man 1d ago
Its telling how bad reddit is that on the science sub there's people still acting like any ai they dont like is some wierd novelty stealing computing power from science.
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u/ackermann 1d ago
LLMs aren’t perfect… but they’re not useless chatbots anymore either.
Besides all the unsolved math problems they’ve figured out (the latest millennium problem is still controversial, but even ignoring that, a dozen Erdos problems),
they’ve discovered some things in biology/medicine too, like this one:
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u/FlagrantDanger 1d ago
"AI" has just replaced "computers" as a lazy catch-all term in the media. And it's become so overused that it has dumbed down technology discourse.
Maybe a silver lining to Trump re-naming it to (the even dumber) "SI" will be that tech reporters stop using the term, and start referring to each individual type of technology. Porbably not, though.
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u/v_a_n_d_e_l_a_y 1d ago
Your first point is correct but calling LLMs glorified chatbots shows ignorance of them.
LLMs at their core are models that have deep "understanding" of language and text, in the same way that image models (useful for detecting cancer, for example) have deep understanding of images.
They can then be post trained to be good as chat bots and that is the most common usage. But they can also be post trained for many other tasks.
And even modern chat bots are tremendously powerful for many applications.
There are lots of criticisms of AI - it's impact on society, the environment and overreliance on it, for sure. But fundamentally these models are a massive breakthrough in a field that has been working in understanding language for decades.
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u/Vailx 2h ago
Cannot even compare LLMs, glorified chatbots, to tools like this they just so different in the way they built and function
All of these are LLMs, a type of AI, and they all share fundamental math. I agree they are different products, but I do think that the subtypes should be more pronounced when possible.
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u/Kaurifish 23h ago
Modeling protein folding, scanning space telescope images, etc. The pros know how to use LLMs.
It’s the people who don’t know how to think who need it taken away.
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u/Sacmo77 1d ago
100% agreed
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u/zippopwnage 1d ago
And it is. Not all AI is bad
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u/round-earth-theory 1d ago
In theory, no AI is bad. None of these tools are "super intelligence" or AGI or any magic words. They're data processing algorithms with a massive hardware backing. That allows them to do interesting things and provide useful insights. Unfortunately, people are easily susceptible to AI psychosis when interacting with a talking parrot box, thinking that it can solve all their problems with no effort.
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u/AmadeusSalieri97 1d ago
They're data processing algorithms with a massive hardware backing
That description is so vague it may as well describe human brains.
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u/round-earth-theory 1d ago
Brains don't operate algorithmically. Otherwise they'd be easy to perfectly model. We can't even model extremely basic life accurately.
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u/ZeroAmusement 23h ago edited 23h ago
I'd say the algorithm isn't explicitly coded anywhere (maybe the closest is our DNA), but every part of the brain follows rules that explain the behavior. Modelling basic life is difficult due to the fidelity of reality, the same applies to the brain. I think it's likely a simplified algorithm that models the general behavior of the brain without needing to simulate to the level molecules does exist.
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u/JustStraightUpTired 1d ago
Brains don't operate algorithmically. Otherwise they'd be easy to perfectly model.
They do, depending on how you define it. If you mean linear step by step math, then AI doesn't fit that definition either, since it processes multiple things at once, not all linearly.
And the reason we can't copy it easily is because it's complex. Frankly, we can't decipher how AI's work either once trained, they end up so complex that it's harder to figure out than it's to train a new one.
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u/Healthy_Mushroom_811 1d ago
Recently a complete fruit fly brain was released. Full connectome. And as far as I remember there are one (or a couple?) fairly accurate C. Elegans simulations for a couple of years already. So we're making definitely good progress of modeling simple model organisms.
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u/NUKE---THE---WHALES 1d ago
There are plenty of complex systems that are made up entirely of algorithmic components but which are functionally impossible to model
Macroeconomies for one
Resistance to modelling is a property of complex systems, not evidence of human brains being fundamentally different to other forms of reasoning machines
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u/CompetitiveAutorun 1d ago
It is used for that. I don't understand why comments like this pop up all the time.
Ai is huge in medicine, arguably one of the original benefits. I was offered a joint collage project on computer usage in medical fields back before it was called ai.
Franky those who are blindly hating on ai muddied water by calling gen ai as all ai. They never specified "art ai", just blanket "ai and now they try to shift blame.
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u/bunker_man 1d ago
It is what it's used for. They just used it for that.
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u/thrownjunk 21h ago
and as a researcher, what we've been using for a long time. honestly the biggest problem is now my hardware spend budget is spiraling out of control so some idiot can make a deepfake of an ex-girlfriend or some stupid thing like that.
so the current AI boom isn't really helping research as much as it could.
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u/bluemountaintrees 1d ago
Yall really aren’t understanding what AI is and how it’s literally just a continuation of the technology we’ve been building for decades and you are only just now hearing about it
AI as a field specifically dedicated to automation of all things at human or better level has been around since 1955
Claude Shannon the founder of the entire field of computing said: “I’m on the side of the machines”
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u/blargyblargy 1d ago
The problem with AI isnt what it could be used for, its what it is being used for by most people. Ways to spy on people, to remove jobs, and ways to eek out every single cent huge corporations can. People wouldnt be so against AI if this is what it was used for, but its not
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u/bluemountaintrees 1d ago
You could say the same thing about every technology used since forever
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u/blargyblargy 1d ago
Yes, you could. Which is why when it's being used for "evil" things it should be called out and fought against. If my government was to hype up Nuclear energy solutions and turn around and make warheads from them I should be pissed. If our governments are touting the benefits AI brings, but then uses it to actively hurt the population, we should be pissed.
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u/baron_von_helmut 22h ago
Yep. If they've done this, then someone somewhere has done it for Ebola or any number of other terrifying viruses.
If AI does end up destroying us all, that's the vector it is going to use.
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u/otherwiseguy 19h ago
AI should be used for whatever it is good at doing that needs doing. Replacing artist jobs is no less moral than replacing scientist jobs. Both take a lifetime of preparation and benefit society. I'd just argue that AI is better at science and coding than it is at generating art that people like.
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u/stewsters 21h ago
AI is just another tool, a series of advanced algorithms. It's a tool that has a lot of uses, and has been in use for many decades.
If you use it for good then the results will be good. If you use it to kill, spread propaganda, and hoard wealth, it will help you do that.
The real issue is we need a system that rewards people looking to use tools to help folks.
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u/Dany0 1d ago
Article makes it sound like traditional AI/ML, not modern transformers/gpt/claude/qwen etc
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u/crashlanding87 1d ago
You are correct, it was not an LLM - their algorithm is based on a strategy called bayesian optimisation. I don't understand it hugely well, but it's basically a statistical model that looks at limited experimental data, and suggests what the next experiment should be to maximise the chance of success.
So, the authors picked some ingredients for their recipe that they believe are probably the right ingredients. They ran a small number of experiments, trying out various recipes using those ingredients. Then the algorithm looked at that data and made suggestions for what recipes to try next.
The idea is to make this kind of research more time and resource efficient.
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u/PreyInstinct 1d ago
I cant read the paper because of the paywall, but I am familiar with Bayesian optimization. This is not a bad explanation as far as I understand it.
Bayesian optimization is usually used to fit a model with many different parameters. You choose some random (or maybe best guess) values for the model parameters, check how well the model fits the data, change one or more of the parameters, then check again. If the model improved, you generally accept the change and iterate again. If it didn't, you revert the change and try something else. This can allow you to "climb" the gradient towards some locally optimal set of parameter values. There are a bunch of tricks, such as trying multiple starting points, tuning the step size (how much parameters change), and extrapolating from previous moves that can help you reach the optimal state faster, or navigate complex systems that might have multiple good or somewhat good solutions.
One of the neat things here is that they put themselves into the Bayesian optimization algorithm, where instead of trying two sets of parameters and calculating a model fit they tried two experiments and measured the results. This isn't so different from how iterative development works in an intuitive way (try something, see if it makes it better, repeat), but I suspect they applied some of the clever tricks to accelerate optimization algorithms, allowing them to make very efficient calculated guesses about what to try next.
If someone wants to shoot me the paper via DM I'd be curious to see exactly how they did it. I am waffling between "that's genius" and "that's so obvious why haven't we been doing this all along", which I think is one of the hallmarks of insightful science. We think we are well reasoned explorers, but our intuition is quite bad and it is often best to submit to systemic thought that we know works rather than to rely on our intuition.
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u/MCPtz MS | Robotics and Control | BS Computer Science 22h ago
From a quick google scholar search, you can get it on https://www.biorxiv.org/content/10.64898/2026.09.17.752370.full.pdf
From the title of the nature publication in the linked article
https://www.nature.com/articles/s41587-026-03331-w
The paper didn't really describe how they created the model... I suspect that will be a different publication.
Motivated by these challenges, we developed AGENT (Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization), a framework that couples high-throughput screening with Bayesian optimization. AGENT guides the data collection in a sample-efficient way, leveraging targeted wet-lab data to iteratively predict and refine formulations, compressing the development cycle from months or years to days.
Not surprising, almost all the main authors are from the "1. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA"
The 1st and 13th author was from "2. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA"
1st author: Jinbi Tian. AI summary of her academic career:
Jinbi Tian is a Ph.D. candidate in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). Her interdisciplinary research bridges artificial intelligence, biomaterials, and nanomedicine, focusing specifically on data-efficient AI frameworks to optimize vaccine stabilization and advanced biointerfaces.
13th author: Mina Konaković Luković
Maybe follow Tian for future updates on the details of their "AGENT"
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u/PreyInstinct 21h ago
Nice, thanks.
Looks like they cite themselves. Here's the preceding paper: https://openreview.net/pdf?id=YrAXARes9d
That established the framework for their AGENT, but they are vague about what specific algorithms they employed with that framework.
My understanding is that, in essence, they run some preliminary experiments, plug that data into their "surrogate model", which isn't really a theoretical model but more similar to a regression that spits out an empirical function that takes the experimental parameters and yields a predicted outcome.
The Bayesian part comes in with their decision making module, which determines what experiments to try next. That function weighs the probability of getting a good outcome (in this case, high transfection efficiency) against the uncertainty of having unexplored regions of the parameter space. You can see how this plays out in their figure showing the results from the cycles of iteration: at first the different formulations perform somewhat randomly. In subsequent iterations it looks like one experiment is the best-guess high performer, while the other experiments are exploratory. In the later iterations the exploratory experiments actually do worse than the initial random experiments, which I figure is the surrogate model getting close to a map of reality, with decent confidence on what the best combination is, then eliminating the unexplored spaces to make sure there aren't other options worth considering.
I'm gonna say this is actually really clever. It combines your knowledge of "known knowns" with "known unknowns" to efficiently explore a design space while also minimizing the risk that you missed a promising alternative.
I wonder how well it holds up to increasingly complex design spaces? Here they limit their exploration to 5 ingredients that they figured were the most promising. Would this work as well if you had 10 parameters worth exploring? How much prior knowledge do you need to get in the ballpark where this method can home in on a solution?
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u/hunterjohnston10 1d ago
Didn’t read the article, but your explanation of BO was pretty much correct. They thrive when your objective is expensive or a black box. In my opinion, the coolest thing about them is how they manage to trade exploration vs exploitation. Traditional gradient descent just tries to get to the best answer, but BO has built in algorithms that force the optimizer to try new regions of the design space instead of just trying to find the best answer based on the information it has. It really makes it an efficient global optimizer.
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u/shellacr 17h ago
What does that have to do with “AI”? I hate how that term is thrown around. AI doesn’t really exist yet, and it’s just a marketing term.
MIT should call it what it was using the technical term, Bayesian optimization, instead of bs marketing lingo. Just my 2 cents.
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u/Newwavecybertiger 1d ago
Ya it seems like the advanced computing lab has its own algorithms. LLM companies want to take credit for all advanced computation but they are two very different things and shouldn't be conflated
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u/FilteringAccount123 22h ago
Yeah it's not, machine learning has been a cornerstone of computational biology for decades now.
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u/lectric_7166 18h ago
Wikipedia, first sentence on machine learning: "Machine learning (ML) is a field of study in artificial intelligence"
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u/galactictock 1d ago
“AI” is a very broad umbrella term. Do not assume LLMs are necessarily being used when you hear the term. Pharmaceutical research is generally not LLM heavy, though often leverages transformers, the underlying architecture behind LLMs.
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u/JustStraightUpTired 1d ago
We aren't assuming that, it's that some people are BECAUSE that's what AI companies want. They are effectively piggybacking off of actual scientific progress by tying their garbage under the same umbrella term.
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u/stargazerAMDG 1d ago
Great news.
But I do want to make one tiny complaint about how we are just calling everything AI now. The work here is good old fashioned machine learning. I feel like the average person that sees this is going to think Claude or OpenAI played a role in this discovery.
And as governments start to debate legislation on AI companies, those companies will absolutely point to every instance of “AI” like this to make it sound like the government wants to stop research like this.
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u/crashlanding87 1d ago
Yeah it's an odd problem. The thing is, these sorts of algorithms were called AI long before LLMs were broadly available. Some have more specific names - machine learning, deep learning etc. But not all.
And tbh, they're not fundamentally that different to LLMs in a lot of cases. An LLM is built on the same sort of architecture as a deep learning algorithm, with extra layers and a very broad training dataset.
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u/galactictock 1d ago
More specifically, this research almost certainly relies upon transformers, the exact architecture that makes LLMs so powerful. Most advances in pharma these days do.
The history of the usage of the term “AI” is interesting. Machine learning was overhyped in the ‘80s and ‘90s and “AI” got a negative association because of that. This is why “machine/deep learning” were more common terms in the 2010s. Now that AI is proving to be highly useful, the term is used everywhere, even for applications that would have been considered “machine/deep learning” a decade ago.
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u/thrownjunk 21h ago
to be fair, saying your research uses "AI" really helps in getting an NSF grant these day. i swear the US gov grant people now only fund grants with AI in the title.
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u/OmgitsJafo 1d ago
And it's never been a meaningful term. But now that it's this huge marketing term for LLMs, alll people are going to hear with headlines like this are "We need to use more ChatGPT! Why aren't my employees using ChatGPT to do something really bank-shattering like this??"
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u/sybrwookie 1d ago
It's not really an odd problem. It's a common problem we've had forever. Bad actors trying to mix bad things into good things so the average person can't tell the difference between the good and bad and pretend it's all good is a tale as old as time.
Heck, for a good example of that, that's exactly what was done to cause the '08 crash. Mix in sub-prime mortgages with great ones, label them all as great, and sell em off in bulk like that.
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u/birbbbbbbbbbbb 1d ago edited 18h ago
Machine learning is a small subset of AI. AI is everything from some if/else decision trees to some graph search algorithms to the large generative neural nets you're talking about. Most people just think the LLMs are the only "AI" because that's all they hear about but it's the name for a very large field of computer science.
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u/CompetitiveAutorun 1d ago
Machine learning is ai.
It's like with cars. Toyota is car, but not cars are Toyota.
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u/SecondhandStatic 1d ago
Also, Claude likely coded this algo.
This "ML isn't AI" BS is just straight up technophobia and has to stop. This is a science sub, we're supposed to be for progress, but instead the top comments are this disingenuous squabbling to fit their agenda.
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u/theArtOfProgramming PhD | Computer Science | Causal Discovery | Climate Informatics 1d ago
Methods like in this work have been called AI long before LLMs existed
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u/merekallas 22h ago
The work here is good old fashioned machine learning.
Which is AI and we called it AI before LLMs.
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u/name99 15h ago
It was always an investor friendly buzz world. It never really fit. It was used to describe AGI type speculative stuff and it's being used now to invoke that image.
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u/MourningWallaby 1d ago
if you go play GTA San Andreas, every car on the road and character on the street not under your control is "AI". but LLM and Generative machines have traken over the name so they can advertise their machines as "thinking"
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u/EnvironmentClear4511 1d ago
They didn't "take over" the name. AI is a very broad term that covers LLMs, machine learning, and a bunch of other tools.
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u/AdhesivenessSlight42 1d ago
The run on sentence that is the headline is meant to be sensational and make people say "wow chat gpt really is good!"
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u/suprmario 1d ago
This is massive news for vaccine logistics in the 3rd world, but really everywhere.
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u/mvea Professor | Medicine 1d ago
New formulation helps RNA vaccines withstand high temperatures
MIT engineers have found a way to stabilize the lipid nanoparticles used to deliver RNA vaccines, which could allow the vaccines to be more widely distributed.
RNA vaccines, which have been proven effective against Covid-19, are now being developed for many other diseases, including cancer. One of the drawbacks to these vaccines is that they require ultracold storage, but researchers from MIT have found a promising way to overcome that limitation.
With help from an AI algorithm, the researchers tweaked the formulation surrounding the lipid nanoparticles that are typically used to deliver mRNA vaccines, making the vaccines more heat-resistant. Using this approach, they formulated vaccines that could remain stable even when stored at room temperature for up to a year, or at nearly 100 degrees Fahrenheit for two months.
When Covid-19 vaccines carried by these particles were administered to mice, they generated just as strong an immune response as an RNA Covid-19 vaccine similar to one developed by Moderna. By using the AI algorithm to predict the optimal formulations for the particles, the researchers were able to cut down the number of experiments they needed to do, which rapidly sped up the development process.
“The real beauty of this algorithm is that we can use it with small data sets,” says Ana Jaklenec, a principal investigator in MIT’s Koch Institute for Integrative Cancer Research. “It’s really hard to run thousands of experiments, so this algorithm allows us to more easily achieve formulations with features that we want — in this case, stability.”
Jaklenec and Robert Langer, the David H. Koch Institute Professor, are the senior authors of the paper, which appears today in Nature Biotechnology.
RNA is a highly fragile molecule, so researchers stabilize it with lipid nanoparticles (LNPs) that protect the RNA from degradation and help it get into cells. However, these RNA-LNP vaccines still need to be kept cold (-20 to -80 degrees Celsius), which makes it difficult to ship them to regions that don’t have cold-storage facilities available.
Making these vaccines more heat-tolerant would not only enable them to be distributed more widely, but could also help researchers develop new vaccines that could be administered through novel methods such as microneedle patches. These patches contain hundreds of vaccine-filled microneedles, which dissolve when the patch is applied to the skin, releasing the vaccine.
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u/Plenty_of_prepotente 1d ago
Note that AI (or ML-based agent if you're not fundraising!) did not come up with the answer, which is what people often think. The scientists used it to reduce the amount of formulation screening they did experimentally:
By using the AI algorithm to predict the optimal formulations for the particles, the researchers were able to cut down the number of experiments they needed to do, which rapidly sped up the development process.
A lot of people's expertise and effort was and is still needed to fully develop this potentially more stable formulation.
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u/Here0s0Johnny 1d ago
The methods are pretty standard machine learning techniques: Gaussian Process Regression, Bayesian Optimization, and Expected Improvement. It's nothing that wasn't possible 10 or 15 years ago, but scientists are forced to call everything AI these days.
The novelty is primarily in the application and experimental integration, but that's less fashionable.
Anyway, a very important practical improvement that should be celebrated!
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u/fgnrtzbdbbt 6h ago
We should use the term "AI" for the thing it is currently used for and call everything else just "machine learning" or "pattern recognition algorithm" in order to avoid confusion.
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u/Memory_Less 1d ago
The tension of AI developing is intense. On the one had there are those in the medical field who are trying to literally save humanity. On the other hand those who fail to control AI and those who want to weaponize. It’s a crazy reality we live.
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u/systembreaker 1d ago
And also on the biotech side of AI there's the very real possibility of these bio focused AIs being used by bad actors to develop a super bio weapon.
It only takes once.
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u/LotusFlare 20h ago
It's worth recognizing that the tools that MIT is using are likely nearly unrecognizable as the tools OpenAI is using to cause panic. The article doesn't even name what was used and it's possible it wasn't even backed by an LLM. We can keep all this helpful stuff while also not developing weapons. The tension is entirely fabricated by a handful of billionaires who want you to think weaponization is inevitable and we have to let them do it to get this good.
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u/tomorrow_comes 1d ago
“Weaponizing” AI I also take to mean what the wealthy owner class wants to do with it - which is create an economy with significantly less white-collar employment, and much wider economic disparity with production and profits even more concentrated to the top. The whole charade of “increasing productivity” can be nothing but that at the end of the day. The bottleneck isn’t that we don’t produce enough, it’s that there aren’t enough customers in existence to make the big line go up 10% every single year. Replacing a hefty chunk of workers with AI will exacerbate that problem, and it’s not sustainable. But execs and investors are only thinking about what they can do for the next few quarters, increase valuations, and get their bag.
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u/galactictock 1d ago
It’s not irrelevant. AI (i.e. non-LLM ML here) has been essential in recent pharmaceutical advancements. This was made possible by AI.
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u/Illustrious-Lime-878 1d ago
Machine learning is standard for decades. Notice they don't mention the use of calculators and electricity as well.
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u/galactictock 1d ago
Even if using machine learning is now standard in pharma, that doesn’t mean it should go unmentioned when the ML was pivotal to the research, which it was here.
Calculators and electricity have been mentioned in scientific research when they were themselves an important part of enabling a discovery or advancement. We don’t normally mention calculators or electricity in a paper when they’re simply background infrastructure, because they aren’t the pivotal technology that made the particular result possible.
The ML wasn’t just a calculator used to crunch the numbers or electricity used to power the computers. The model was part of the process that enabled the researchers to identify and optimize the relevant candidates. In that sense, the advancement in AI/ML is directly relevant to why this particular advancement in pharma was possible.
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u/theArtOfProgramming PhD | Computer Science | Causal Discovery | Climate Informatics 1d ago
AI has been a field including ML since the 70s or even earlier. Its entrance into the common parlance being recent or that colloquial AI has recently aligned with technical AI doesn’t change that.
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u/HomsarWasRight 1d ago
Exactly, the article says “With help from an AI algorithm…”.
People see “used AI” and assume they’re talking about ChatGPT or some other LLM, when they almost certainly used custom Machine Learning models made for testing formulations or the like.
AI branding has been a disaster.
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u/garanvor 1d ago
It is actually relevant in this case, it only isn’t the usual Large Language Model the public conflates with other types of neural network software. It helps to read the article sometimes.
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u/dgellow 1d ago
The team finding is the relevant part, mentioning prominently the fact they use some AI as part of their process is PR for the university. Everybody in that field has been using ML as part of their processes since a while. Those university news articles are pretty much always for PR
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u/bunker_man 1d ago
Its pretty relevant when a lot of people fell for propaganda that ai is just some wierd novelty and has no practical uses. Ironically, the idea that its odd to include in the title is something people only think because in their propagandized view of the world it should be glossed over.
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u/mrpanicy 22h ago
MIT used machine learning algorithms to develop new recipes for mRNA. Please stop legitimizing the marketing speak. AI doesn't yet exist in any real sense at this point and we need to stop pretending that it does.
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u/choogbaloom 1d ago
How did they make sure the changes don't make it too resistant to the body breaking it down?
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u/trashfruitass 1d ago
The changes come from the formulation ratio, which includes the same series of lipids found in the Covid formulations. Think about it this way, the optimized ratio of the same components boosted the stability of the particles in storage buffer (and they also mention non aqueous for the cool patch stuff), it does not attempt and shouldn’t change how the particles themselves function. The LNP still reaches target cells where the mRNA can eventually release and do its job. They use a term in the LNP field called rationale design (so original I know) that means you use components and structure design that you generally understand is safe and effective. So all those lipids will break down in the same way they always have, even if there is a slight concentration difference in each.
The invivo data is very important because that suggests that the particles are just as effective in whatever response signal they are measuring (antibody response, protein production and such) compared to the original formulations they are testing against. If they are getting similar potency that implies a similar number of particles experience cellular uptake, particle breakdown and thus the protein production. In short, the storage buffer is such a different environment from the bloodstream that they are two very different stability problems! They just tackled the storage one which has been a big question for the LNP field for awhile.
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u/schroedingerx 1d ago
Using an LLM is not the most relevant part of the headline. The accomplishment is, and it belongs to the researchers not the tools.
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u/SamKhan23 23h ago
I feel I could be missing some part of the article but I don’t see any mention of LLMs? Just the nebulous “AI algorithm”
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u/DiscountConsistent 21h ago
The paper is here https://www.biorxiv.org/content/10.64898/2026.09.17.752370v1.full.pdf
To accelerate the identification of optimal excipient formulations for solid-state mRNA-LNPs, we developed AGENT, a data-efficient experiment design pipeline based on Bayesian optimization algorithms. AGENT leverages prior experimental outcomes to iteratively refine its predictions, effectively balancing exploration of the design space with exploitation of high-performing formulations
No mention of LLMs
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u/BeartholomewTheThird 1d ago
At least they got the headline accurate. It didn't say "AI developed new vaccine". It said "MIT used AI".
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u/lectric_7166 18h ago
Yeah, so nothing wrong with the headline despite the habitual and ritualistic griping which must occur whenever AI is mentioned.
Decades ago, the headline would've been "MIT used personal computers to..." basically just highlighting a successful use case of a technology.
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u/Mitochondria420 1d ago
If this works out, this is a huge advancement. RNA is not stable above freezing so this would be great for not only vaccines but health science as a whole.
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u/djradcon 21h ago
Everyone is upset at the use of the term AI here. To be pedantic, it is technically correct usage. AI is the largest bubble on the Venn diagram, before we move into more specialized fields like Machine Learning or Deep Learning.
Even if this were taught across public schools as part of standard curriculum, media would still use the term which generates the most clicks, in this case divisive AI.
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u/Humble-Violinist6910 21h ago
The image says "INJIECTION ONLY." Despite being in English. So the image is fake. How gullible are we...?
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u/FrugosPeach 20h ago
It’s probably just a stock photo liquid medicine bro. I’m sure there isn’t a photographer running to take a picture of a scientist holding their work every time there’s a new thing at MIT.
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u/Mental-Scientist-393 1d ago
From my experience, this kind of research was never referred to as AI until maybe ~5 years ago. It was always called "Machine Learning."
There's a reaction to RFK announcing an initiative to combine healthcare dataset and use AI to answer questions that makes me think a lot of people think the AI in healthcare research is LLMs, which for the most part it is not.
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u/lurpeli 1d ago
LLMs aren't even really AI. They aren't intelligent, they just predict tokens. Turns out if you have the corpus of human communication available to learn from you can convincingly appear human. I think most of us these days would say the Turing test isn't really a good measure of artificial intelligence
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u/pm_me_ur_knowledge 23h ago
To add my two cents to the debate here: "AI" is not a very descriptive term. The title should be "MIT used a purpose-built machine learning algorithm to help..." AI can mean a lot of things, most of which should be called "machine learning algorithm for x purpose." If it's an LLM, just say that, don't say "AI". If it's a generative image algorithm, say that. If you just say "AI", that conflates too many different things. And the problem is that some of those things are awesome (like in the linked article) and some are really horrible (like LLMs and generative image algorithms). And I feel like a lot of the frustration in this thread is related to the latter taking credit and credibility from the latter, because it's all "AI".
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u/FirelightMLPOC 1d ago
Is this AI? GenAI? MLA? A glorified chatbot? Fuckin’ hate how most articles conflate all of those as the same thing even though most if not all of them are completely different from eachother.
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u/igna92ts 18h ago
Kinda infuriating that people use umbrella terms like AI so now people will think LLMs are doing this stuff that's actually valuable.
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u/GhostDoggoes 22h ago
This is what AI should be used for. Not to lower IQ in schools and support lonely people
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u/TheBlockChainVillage 1d ago
Hope this makes it considerably cheaper also and not all profits flow to one place.
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u/Nemisis_the_2nd 1d ago
Everyone is focusing on the AI aspect, but overlooking the fact they managed to break the need for a cold chain for vaccines. This is a complete game-changer for half the world, if not more. Even the developed world will likely see significant benefits from something like this.
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u/AffectionateSoil2991 1d ago
This is what AI should be used for. Leave the creative work to the humans and give the work to the computers.
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u/ThrowawayALAT 22h ago
Brilliant.
This breakthrough could completely transform global healthcare by eliminating cold-chain logistics barriers that have long hindered vaccine distribution in developing nations.
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u/ArtroyomsAK 22h ago
What Ai should be doing instead of stealing artists" jobs and making propaganda
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