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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.
Also they already got their nobel prize, so the marketing benefit is already maxed 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. 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 p…
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.
David Baker is the GOAT of that field, it should have been awarded to him only.
>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 p…
>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 p…
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.
> 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?
I'm guessing Google can't make money on them.
You can do more with the weights than just the protein structures. You can design new proteins. So no, the database is not enough. But the weights and a university cluster are enough.
Earlier quoted context omitted.
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.
>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 p…
To SOLVE it would easily win the Nobel, possibly the Turing and more.
Designer proteins would change the world and medicine also in unimaginable ways.