Would be very useful if one they used it to predict the structure and interaction of the known variants to.
Would be very helpful when predicting if a mutation on a protein would lead to loss of function for the protein.
AlphaFold 3 predicts the structure and interactions of life's molecules
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Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#222Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…
Even if we don’t understand the models themselves, you can still use them as a basis for understanding
For example, I have no idea how a computer works in every minute detail (ie, exactly the physics and chemistry of every process that happens in real time), but I have enough of an understanding of what to do with it, that I can use it as an incredibly useful tool for many things
Definitely interesting times!
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#223Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…
I'm not a scientist by any means, but I imagine even accurate opaque models can be useful in moving the knowledge forward. For example, they can allow you to accurately simulate reality, making experiments faster and cheaper to execute.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#224The improvement on predicting protein/RNA/ligand interactions might facilitate many commercially relevant use cases. I assume pharma and biotech will eagerly get in line to use this.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#225Earlier quoted context omitted.
What if the underlying principles of the universe are too complex for human understanding but we can train a model that very closely follows them?
That sounds like useful engineering, but not useful science.
I think that a lot of scientific discoveries originate from initial observations made during engineering work or just out of curiosity without rigour.
Not saying ML methods haven't shown important reproducibility challenges, but to just shut them down due to not being "useful science" is inflexible.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#226Very cool, and what’s cooler is this rap about alphafold3 https://heymusic.ai/blog/news/alphafold-3
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#227Earlier quoted context omitted.
- "Imagine the goodwill for humanity for releasing these pure research systems for free." The entire point[0] is that they want to sell an API to drug-developer labs, at exclusive-monopoly pricing. Those labs in turn discover life-saving drugs, and recoup their costs from e.g. parents of otherwise-terminally-ill children—again, priced as an exclusive monopoly. [0] As signaled by "it is not possible to obtain structur…
What makes this such a "deeply broken situation"? I agree that late-stage capitalism can create really tough situations for poor families trying to afford drugs. At the same time, I don't know any other incentive structure that would have brought us a breakthrough like AlphaFold this soon. For the first time in history, we have ML models that are beating out the scientific models by huge margins. The very fact that t…
Late-stage capitalism didn't bring us AlphaFold, scientists did, late-stage capitalism just brought us Alphabet swooping in at literally the last minute. Socialize the innovation because that requires potential losses, privatize the profits, basically. It's reminiscent of "Heroes of CRISPR," where Doudna and Charpentier are supposedly just some middle-men, because stepping in at the last minute with more funding is really what fuels innovation.
AlphaFold wasn't some lone genius breakthrough that came out of nowhere, everything but the final steps were basically created in academia through public funding. The key insights, some combination of realizing that the importance of sequence to structure to function put analyzable constraints on sequence conservation and which ML models could be applied to this, were made in academia a long time ago. AlphaFold's training set, the PDB, is also a result of decades of publicly funded work. After that, the problem was just getting enough funding amidst funding cuts and inflation to optimize. David Baker at IPD did so relatively successfully, Jinbo Xu is less of a fundraiser but was able to keep up basically alone with one or two grad students at a time, etc. AlphaFold1 threw way more people and money to basically copy what Jinbo Xu had already done and barely beat him at that year's CASP. Academics were leading the way until very, very recently, it's not like the problem was stalled for decades.
Thankfully, the funding cuts will continue until research improves, and after decades of inflation cutting into grants, we are being rewarded by funding cuts to almost every major funding body this year. I pledge allegiance to the flag!
EDIT: Basically, if you know any scientists, you know the vast majority of us work for years with little consideration for profit because we care about the science and its social impact. It's grating for the community, after being treated worse every year, to then see all the final credit go to people or companies like Eric Lander and Google. Then everyone has to start over, pick some new niche that everyone thinks is impossible, only to worry about losing it when someone begins to get it to work.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#228For a couple of years I've been expecting that ML models would be able to 'accelerate' bio-molecular simulations, using physics-based simulations as ground truth. But this seems to be a step beyond that.
When I competed in CASP 20 years ago (and lost terribly) I predicted that the next step to improve predictions would be to develop empirically fitted force fields to make MD produce accurate structure predictions (MD already uses empirically fitted force fields, but they are not great). This area was explored, there are now better force fields, but that didn't really push protein structure prediction forward.
Another approach is fully differentiable force fields- the idea that the force field function itself is a trainable structure (rather than just the parameters/weights/constants) that can be optimized directly towards a goal. Also explored, produced some interesting results, but nothing that woudl be considered transformative.
The field still generally believes that if you had a perfect force field and infinite computing time, you could directly recapitulate the trajectories of proteins folding (from fully unfolded to final state along with all the intermediates), but that doesn't address any practical problems, and is massively wasteful of resources compared to using ML models that exploit evolutionary information encoded in sequence and structures.
In retrospect I'm pretty relieved I was wrong, as the new methods are more effective with far fewer resources.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#229Important caveat: it's only about 70% accurate. Why doesn't the press release say this explicitly? It seems intentionally misleading to only report accuracy relative to existing methods, which apparently are just not so good (30%, 50% in various settings). https://www.fastcompany.com/91120456/deepmind-alphafold-3-dn...
That's pretty good. Based on the previous performance improvements of Alpha-- models, it'll be nearing 100% in the next couple of years.
> it'll be nearing 100% in the next couple of years.
What are you basing this on? There is no established "moores law" for computational models.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#230Earlier quoted context omitted.
Just "Alpha-- models" in general?? That's not a remotely reasonable way to reason about it. Even if it were, why should it stop DeepMind from clearly communicating accuracy?
The way I think about this (specifically, deepmind not publishing their code or sharing their exact experimental results): advanced science is a game played by the most sophisticated actors in the world. Demis is one of those actors, and he plays the games those actors play better than anybody else I've ever seen. Those actors don't care much about the details of any specific system's accuracy: they care to know that…
I think it's important to qualify that the relevant "game" is not advanced science per se; the game is business whose product is science. The aim isn't to do novel science; it's to do something which can be advertised as novel science. That isn't to cast aspersions on the personal motivations of Hassabis or any other individual researcher working there (which itself isn't to remove their responsibilities to public understanding); it's to cast aspersions on the structure that they're part of. And it's not to say that they can't produce novel or important science as part of their work there. And it's also not to say that the same tension isn't often present in the science world - but I think it's present to an extreme degree at DeepMind.
(Sometimes the distinction between novel science and advertisably novel science is very important, as seems to be the case in the "new materials" research dopylitty linked to in these comments: here https://www.404media.co/google-says-it-discovered-millions-o...)