>DeepMind started developing AlphaFold in 2018. In 2020, it was recognized as a solution to humanity's 50-year-old "protein folding problem," which sought to answer how amino acids automatically fold into complex 3D shapes.
This is not even close to reality. AlphaFold is not a solution to the protein folding problem, it's (useful) pattern matching to the end state of solved folded protein (in situations that can be pattern matched). If this is considered "solving the protein folding problem" then X-ray crystallography solved it first, 75 years ago.
There is essentially zero "how" information coming to us from AlphaFold. This type of reporting is incorrect, and irresponsible to the folks that are still working on that how problem.
Pissed me off since day one that got by with that "solving" shit. Not to downplay what they did, but it had little to do with the problem as it's been understood to mean.
The kind of solution you want may not be possible.
There is no guarantee that a tractable method exists to analytically invert protein folding. Data-driven methods like alphafold may be the only option.
The dedicated research team is getting moved to other areas, the Alphafold system itself and its database will likely continue to get maintained, but this means there probably won't be an "Alphafold 4".
It's probably because they hit a dead end with their approach in terms of improvements. You can only get so far with trying to model a physical system with an insane number of degrees of freedom from simulation (and augmented) data.
As per the article they're slowly shuttering the project, but I assume AlphaFold itself is still available and usable?
I assume that AlphaFold isn't perfect, but surely after so many years on it most of the useful juice had been squeezed in terms of making it a useful tool?
The obvious question to any business leader is "why?" Why deploy resources to a project? Is this project central to our current or future revenue streams? If not, toss it out.
> DeepMind started developing AlphaFold in 2018. In 2020, it was recognized as a solution to humanity's 50-year-old "protein folding problem," which sought to answer how amino acids automatically fold into complex 3D shapes. Those shapes determine the biological role of a protein. Scientists had identified the structures of roughly 170,000 proteins over the past 50 years, using tools and techniques like X-ray and nuclear magnetic resonance. The AlphaFold team took information from those previous work and then fed it to their AI to train AlphaFold.
> In 2021, Nature published the papers with AlphaFold's methodology and the structure predictions of the entire human proteome, or the complete set of proteins expressed by our species. DeepMind then launched the AlphaFold Protein Structure Database, giving researchers free access to over 200 million protein structure predictions.
Why is there no information on corroborations of the predictions? Anyone can make predictions. Surely there must have been teams picking predicted structures out of the database and comparing them to actual molecules?
I know you're saying this tongue in cheek. But the reason they're shuttering Alphafold is (likely) to assign those engineers and their expertise to Generative AI initiatives like Gemini.
>DeepMind started developing AlphaFold in 2018. In 2020, it was recognized as a solution to humanity's 50-year-old "protein folding problem," which sought to answer how amino acids automatically fold into complex 3D shapes.
This is not even close to reality. AlphaFold is not a solution to the protein folding problem, it's (useful) pattern matching to the end state of solved folded protein (in situations that can be pattern matched). If this is considered "solving the protein folding problem" then X-ray crystallography solved it first, 75 years ago.
There is essentially zero "how" information coming to us from AlphaFold. This type of reporting is incorrect, and irresponsible to the folks that are still working on that how problem.
Pissed me off since day one that got by with that "solving" shit. Not to downplay what they did, but it had little to do with the problem as it's been understood to mean.
And to be clear I do think it was nobel worthy.
I do not think it was Nobel worthy for Hassam et. al.
David Baker is the GOAT of that field, it should have been awarded to him only.
The kind of solution you want may not be possible.
There is no guarantee that a tractable method exists to analytically invert protein folding. Data-driven methods like alphafold may be the only option.
That has nothing to do with the fact that the article is inaccurate.
Tell that to the nobel prize committee.
The article is fine. AlphaFold is widely regarded as "solving" protein folding.
Yes and correct me if I'm wrong, but they never did deeper work on dynamics. I think that's very telling.
The dedicated research team is getting moved to other areas, the Alphafold system itself and its database will likely continue to get maintained, but this means there probably won't be an "Alphafold 4".
To learn more about AlphaFold and its importance, I would recommend the Veritasium video[0]: AlphaFold - The Most Useful Thing AI Has Ever Done
[0]: https://www.youtube.com/watch?v=P_fHJIYENdI
> DeepMind then launched the AlphaFold Protein Structure Database, giving researchers free access to over 200 million protein structure predictions.
Is this existing database enough for researchers?
It's probably because they hit a dead end with their approach in terms of improvements. You can only get so far with trying to model a physical system with an insane number of degrees of freedom from simulation (and augmented) data.
More likely because the entire Google organization is refocusing on LLMs.
Also they already got their nobel prize, so the marketing benefit is already maxed out.
As per the article they're slowly shuttering the project, but I assume AlphaFold itself is still available and usable?
I assume that AlphaFold isn't perfect, but surely after so many years on it most of the useful juice had been squeezed in terms of making it a useful tool?
The obvious question to any business leader is "why?" Why deploy resources to a project? Is this project central to our current or future revenue streams? If not, toss it out.
guess we need all that compute for AI?
Is there a lot more work to be done?
False title. The shutdown is of the AlphaFold team.
Earlier: https://news.ycombinator.com/item?id=49096841
> DeepMind started developing AlphaFold in 2018. In 2020, it was recognized as a solution to humanity's 50-year-old "protein folding problem," which sought to answer how amino acids automatically fold into complex 3D shapes. Those shapes determine the biological role of a protein. Scientists had identified the structures of roughly 170,000 proteins over the past 50 years, using tools and techniques like X-ray and nuclear magnetic resonance. The AlphaFold team took information from those previous work and then fed it to their AI to train AlphaFold.
> In 2021, Nature published the papers with AlphaFold's methodology and the structure predictions of the entire human proteome, or the complete set of proteins expressed by our species. DeepMind then launched the AlphaFold Protein Structure Database, giving researchers free access to over 200 million protein structure predictions.
Why is there no information on corroborations of the predictions? Anyone can make predictions. Surely there must have been teams picking predicted structures out of the database and comparing them to actual molecules?
There's plenty of information
https://en.wikipedia.org/wiki/CASP
They realised it's time... Gemini to follow?
I know you're saying this tongue in cheek. But the reason they're shuttering Alphafold is (likely) to assign those engineers and their expertise to Generative AI initiatives like Gemini.