Sigh, it's such a brilliant tool it does not need this massive overselling. But by any reasonable definition is does not "predict the structure of almost all known proteins"
The 2024 #NobelPrize laureates in chemistry Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Read more about their story: https://t.co/nWxcZs6wqC
Big redesign coming to @visualPDE thanks to enormous efforts from @bj_w95 and @Pecnut. As an unrelated bit of fun, here's a diffusively-coupled Lorenz simulation. I call it "Butterflies lost in the soup."
Link to interactive simulation: https://t.co/cbsF5uhwgv
Fantastic 3 year postdoc in lovely Durham working on developing methods in fluid dynamics with an excellent colleague. Great opportunity to do some meaningful and deep fluid dynamics/scientific computing work!
https://t.co/bLLIwI2uFG
We can model these CMEs to try and predict their arrival time at Earth. Currently we think they should arrive this evening and over the weekend (10/11 May). Therefore a G4 (severe geomagnetic storm) warning has been issued - the first time since 2005 (4/n)
👇👇, The setting up the protein data bank is (in my view ) hands down the biggest event in protein science. The efficient sharing of data should be the key consideration in any scientific field.
And it will be hundreds of PhDs running and interpreting experiments which will actually help us understand how proteins move in their natural environment
FUND THEM