If you set up the static site generator as a CI/CD action in you favorite git provider, can work with both hosted GitHub, GitLab, etc. or self hosted Forgejo [1], you have both version control for your blog as well as an automatic way of publishing.
Sure, the UX is not that great as with a dedicated interface like substack, but building a Hugo site is really just editing markdown files anyway, most mobile git enabled editors should be able to do that.
I think Voyager is not just a space exploration project, but more a demonstration of technical ingenuity. Sure, the probe probably collected great data just by being where no other probe was before, but to be real: I don't know nearly enough about space exploration research to get excited about the results and mostly just looked at the pictures.
What amazes me about the device and the mission as a whole is the sheer challenge of operating a device that is so far away, you have to use the prefix light to make the scale understandable. I like devices, that have been engineered to something close to perfection. I think aircraft a cool because they so very rarely fail. I think that pacemakers are amazing, because they can not fail. This is another example, and perhaps one of the greatest: a spacecraft that is running for 40+ years in the harshest environments and still works.
And that's not even touching the emotional and somewhat existential thoughts that comes with the scale and distance this little guy has traveled.
For the most part, when working with hardware, I find this to be quite typical. A spec sheet, if one is lucky maybe an application note tackling a tangentially similar problem and thats usually it. Unfortunately open source hardware tends to be much less of a thing than software.
While I am skeptical about yesterdays award in physics, these are totally deserved and spot on. There are few approaches that will accelerate the field of drug development and chemistry as a whole in a way that the works of these three people will. Congratulations!
> There are few approaches that will accelerate the field of drug development and chemistry as a whole in a way that the works of these three people will.
As the author of one such approach, I'm skeptical.
AlphaFold 2 just predicts protein structures. The thing about proteins is that they are often related to each other. If you are trying to predict the structure of a naturally occurring protein, chances are that there are related ones in the dataset of known 3D structures. This makes it much easier for ML. You are (roughly speaking) training on the test set.
However, for drug design, which is what AlphaFold 3 targets, you need to do well on actually novel inputs. It's a completely different use case.
Protein structures are similar to each other because of evolution (protein families exist because of shared ancestry of protein coding genes). It's not a weird coincidence that helps ML; it's inherent in the problem. Same with drug design -- very, very, few drugs are "novel" as opposed to being analogues of something naturally in the body.
They're referring to the structure of the protein when a drug is bound, that's what's novel. Novel as in, you can't think of it as "just" interpolation between known structures of evolutionarily related proteins.
That said I'm not sure that's entirely fair, since Alphafold does, as far as I know, work for predicting structures that are far away from structures that have previously been measured.
You're quite wrong about small molecule drug structures. Historically that has been the case but these days many lead structures are made by combinatorial chemistry and are not derived from natural products.
But even drugs made by combinatorial chemistry still generally end up being analogues of natural products even if they aren't derived from them. As Leslie Orgel said "Evolution is cleverer than you are"; chemists are unlikely to discover a mechanism of action that millions of years of evolution hasn't already found.
I... Don't think that's right? Although I would appreciate being corrected with some good sources on this. It's a fast moving field and combinatorial chemistry is still new enough that many recently published structures wouldn't have used it.
I'm well aware of the impact of natural products and particularly plant secondary metabolites in drug discovery. I'm also aware of combinatorial synthesis occasionally hitting structures that are close to natural products.
But from first principles, why would you need to limit yourself to that subset of molecular space?
Obviously, your structure will need to look vaguely biochemical to be compatible with the bodies chemical environment, but natural products are limited to biochemically feasible syntheses, and are therefore dominated by structures derived from natural amino acids and similar basic biochemical building blocks.
For a concrete example off the top of my head, I'm not aware of any natural diazepines - the structure looks "organic" but biochemistry doesn't often make 7-rings, and those were made long before combinatorial chemistry. Might be wrong on this one, since there's so much out there, but I think it holds.
Perhaps we are using "structure" in different senses. Yes, it is possible to generate a molecule with a chemical structure unlike any biological molecule and have it bind to a protein, but it can only do so if its 3D structure is analogous to what naturally binds there. Natural products are a source of drugs because evolution has already done this work for us.
Yes, the chemical structures can look very different when drawn in the 2D manner, but that's why 2D structures aren't very useful for understanding binding, much as primary sequences of proteins aren't that useful. Morphine and fentanyl bind to μ-opioid receptors, just like what naturally binds there (endorphins and enkephalin). But if they are binding to the same receptor, they have to have similar structures in the biologically meaningful 3D sense (at least where they bind).
It appears that you were merely saying that ligands must adopt a 3D conformation that's complementary to the receptor. Sure. That's the entire premise of molecular docking software.
But there can be very dissimilar ligands (like morphine and fentanyl) binding the same receptors. A major goal of drug discovery is to find such novel binders, not to regurgitate known ones.
> It's not a weird coincidence that helps ML; it's inherent in the problem.
This depends on the application. If you are trying to design new proteins for something, unconstrained by evolution, you may want a method that does well on novel inputs.
> Same with drug design
Not by a long shot. There are maybe on the order of 10,000 known 3D protein-ligand structures. Meanwhile, when doing drug discovery, people scan drug libraries with millions to billions of molecules (using my software, oftentimes). These molecules will be very poorly represented in the training data.
I was just wondering when they were going to award the alphafold2 guys the nobel after after seeing Hinton win the physics one. 100% agree, all three of them totally deserve this one. Baker's lab is pretty much keeping Deepmind in check at this point and ensuring open source research is keeping up. Hats off
Baker has been in the protein folding game for a long time and was the leader before Alphafold came in... His generative paper came out what last year (2023)?
As someone in the drug discovery business I’m skeptical as I’ve seen many such “advances” flop.
I remember when computer aided drug design first came out (and several “quantum jumps” along the way). While useful they failed often at the most important cases.
New drugs tend to be developed in spaces we know very little about. Thus there is nothing useful for AI to be trained on.
Nothing quite like hearing from the computational scientist “if you make this one change it will improve binding by 1000x”. Then spending 3 weeks making it to find out it actually binds worse.
They were not equal contributors to the seminal paper that got the prize.
From another post in this thread:
"These authors contributed equally: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Demis Hassabis"
Sure, the UX is not that great as with a dedicated interface like substack, but building a Hugo site is really just editing markdown files anyway, most mobile git enabled editors should be able to do that.
[1]: https://home.futuretim.io/posts/hugo_build_and_post/