I wonder if they used the output of alpha fold? Remember that Deepmind published the 3D structure of hundreds of millions of proteins for FREE. Imagine if they walled off that data behind an Elsevier like subscription wall? They shoould credit Deep Mind at least
AlphaFold is regurgitating structural information from 10s of thousands of experimental structures acquired at great cost and published to the PDB for free, with no license restrictions of any kind.
You make a fair point but much of that work was funded by public grants, while AlphaFold was privately funded. Those generally come with different expectations
looks like it - it's such a minor and brief mention in the paper for the article to focus on it so much lol. They probably should have cited it, looks like they decided it was minor enough (or forgot) that they didn't put it in their software used/citation. Super commonly used though I wouldn't be stunned if most of its uses never got cited- just a quick check if it thinks deleting some section or doing some sort of fusion is gonna cause a problem, or if you've got something without a PDB structure finding a site to mess with that looks like it's not gonna cause any problems. You can't count on it blindly obviously but it's super helpful. Like if it's pretty confident about some section of a protein it hasn't seen before, the weird stuff you're studying might not be folded properly by the model, but if you want to stick a handle onto the protein to grab it with whatever it can let you know where's the least likely to be a waste of time and money to try.
While I (loosely) understand the concept of using a custom (foundational?) machine-learning model to explore some problem-space and devise solutions, I don't understand why it says they used "AI" to "visualize" a structure. A layperson is going to think they simply asked ChatGPT to solve the problem for them and it just worked and now OpenAI owns the cure for Alzheimer's.
...I ask because bio/chem visualization and simulation was a solved problem back in the 1980s (...back when bad TV shows used renders of spinning organic-chemistry hexagons on the protagonist's computer as a visual-metaphore for doing science!).
The popular press likes to call anything ML related "AI" since presumably that's what they think the public wants to read about.
In any case, the percentage of the population who knows the difference between a transformer, or diffusion model, or a bespoke protein folding model is going to be tiny, so calling it all "AI" does make practical sense despite being a bit misleading (it's not all ChatGPT).
Just to be clear though, the "AI" (AlphaFold) isn't being used to visualize the protein - it's used to guess the 3-D structure of the protein (via folding rules), which can then be visualized.
Protein folding is one of the oldest and hardest problems in computational biology. It is fair to describe the result of protein folding as a 3D model/visualization of the protein. DeepMind's AlphaFold was a big breakthrough in determining how arbitrary structures are folded. Not always correct, but when it is, often faster and cheaper than traditional methods. I believe the latest versions of AlphaFold incorporate transformers, but it's certainly not a large language model like ChatGPT.
