Google Shuts Down Nobel Prize Winning AlphaFold to Focus on Gemini
Google DeepMind has disbanded the team behind the Nobel Prize-winning AlphaFold project to redirect resources toward Gemini. Key members have been reassigned to internal projects or moved to Isomorphic Labs, while others departed for companies like Anthropic. This strategic shift marks the end of an era for the AI program that solved the protein folding problem, as Google prioritizes its broader generative AI ambitions over its previous grand challenges.
Our strategy over the last nine years has been to focus on grand challenges... a concrete goal every project is focused on. The strategy has evolved.
- colingauvin
>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.
- paxys
The entire team behind AlphaFold, including John Jumper, went to Anthropic, and of course Hassabis is handling larger AI efforts at Google, so it was only a matter of time.
- AntonyGarand
To learn more about AlphaFold and its importance, I would recommend the Veritasium video[0]: AlphaFold - The Most Useful Thing AI Has Ever Done
- jrflo
> 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?
- geremiiah
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.