See for example the AlphaFold2 presentation linked here: https://predictioncenter.org/casp14/doc/presentations/2020_1.... Some samples that point out where most of the innovations are NOT just "huck a transformer at it":

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Physical insights are built into the network structure, not just a process around it

- End-to-end system directly producing a structure instead of inter-residue distances

- Inductive biases reflect our knowledge of protein physics and geometry

- The positions of residues in the sequence are de-emphasized

- Instead residues that are close in the folded protein need to communicate

- The network iteratively learns a graph of which residues are close, while reasoning over this implicit graph as it is being built

What went badly:

- Manual work required to get a very high-quality Orf8 prediction

- Genetics search works much better on full sequences than individual domains

- Final relaxation required to remove stereochemical violations

What went well

- Building the full pipeline as a single end-to-end deep learning system

- Building physical and geometric notions into the architecture instead of a search process

- Models that predict their own accuracy can be used for model-ranking

- Using model uncertainty as a signal to improve our methods (e.g. training new models to eliminate problems with long chains)

====

Also you can read the papers, e.g. https://www.nature.com/articles/s41586-019-1923-7 (available if you search the title on Google Scholar; also https://www.nature.com/articles/s41586-021-03819-2_reference...). There is actual, real good science, physics, and engineering going on here, as compared to e.g. LLMs or computer vision models that are just trained on the internet, and where all the engineering is focused on managing finicky training and compute costs. AlphaFold requires all this and more.

EDIT: Basically, the article makes it sound like deep models just allowed scientists to sidestep all the complicated physics and etc and just magically solve the problem, and while this is arguably somewhat correct for computer vision and much of NLP, this is the exact opposite of the truth for AlphaFold.

From the text:

> as you're reading this, there are approximately 20,000 different types of proteins working inside your body.

From https://biologyinsights.com/how-many-human-proteins-are-ther...

"The human genome contains approximately 19,000 to 20,000 protein-coding genes. While each gene can initiate the production of at least one protein, the total count of distinct proteins is significantly higher. Estimates suggest the human body contains 80,000 to 400,000 different protein types, with some projections reaching up to a million, depending on how a “distinct protein” is defined."

Plus, that's just in the human DNA. In your body are a whole bunch of bacteria, adding even more types of protein.

> The actual number of protein molecules? Billions. Trillions if we're counting across all your cells.

There are on average 10 trillion proteins in a single cell. https://nigms.nih.gov/biobeat/2025/01/proteins-by-the-number... There are over 30 trillion human cells in an adult. https://pmc.ncbi.nlm.nih.gov/articles/PMC4991899/ . That's about 300 septillion proteins in the body. While yes, that's "trillions" in some mathematical sense, in that case it's also "tens" of proteins.

(The linked-to piece later says "every single one of your 37 trillion cells", showing that "trillions" is far from the correct characterization. "trillions of trillions" would get the point across better.)

> Each one has a specific job.

Proteins can do multiple jobs, unless you define "job" as "whatever the protein does."

Eg, from https://pmc.ncbi.nlm.nih.gov/articles/PMC3022353/

"many of the proteins or protein domains encoded by viruses are multifunctional. The transmembrane (TM) domains of Hepatitis C Virus envelope glycoprotein are extreme examples of such multifunctionality. Indeed, these TM domains bear ER retention signals, demonstrate signal function and are involved in E1:E2 heterodimerization (Cocquerel et al. 1999; Cocquerel et al. 1998; Cocquerel et al. 2000). All these functions are partially overlapped and present in the sequence of <30 amino acids"

> And if even ONE type folds wrong, one could get ... sickle cell anemia

Sickle cell anemia is due to a mutation in the hemoglobin gene causing a hydrophobic patch to appear on the surface, which causes the hemoglobins to stick to each other.

It isn't caused by misfolding. https://en.wikipedia.org/wiki/Sickle_cell_disease

(I haven't researched the others to see if they are due to misfolding.)

> Your body makes these proteins perfectly

No, it doesn't. The error rate is quite low, but not perfect. Quoting https://pmc.ncbi.nlm.nih.gov/articles/PMC3866648/

"Errors are more frequent during protein synthesis, resulting either from misacylation of tRNAs or from tRNA selection errors that cause insertion of an incorrect amino acid (misreading) shifting out of the normal reading frame (frameshifting), or spontaneous release of the peptidyl-tRNA (drop-off) (Kurland et al. 1996). Misreading errors are arguably the most common translational errors (Kramer and Farabaugh 2007; Kramer et al. 2010; Yadavalli and Ibba 2012)."

> Then AI companies showed up in 2020 and said "we got this" and solved it in an afternoon.

They didn't simply "show up" in 2020. Google DeepMind was working on it since 2016 or so. https://www.quantamagazine.org/how-ai-revolutionized-protein...

> we're DESIGNING entirely new proteins that have never existed in nature

We've been designing new proteins that have never existed in nature for decades. From https://en.wikipedia.org/wiki/Protein_design

"The first protein successfully designed completely de novo was done by Stephen Mayo and coworkers in 1997 ... Later, in 2008, Baker's group computationally designed enzymes for two different reactions.[7] In 2010, one of the most powerful broadly neutralizing antibodies was isolated from patient serum using a computationally designed protein probe.[8] In 2024, Baker received one half of the Nobel Prize in Chemistry for his advancement of computational protein design, with the other half being shared by Demis Hassabis and John Jumper of Deepmind for protein structure prediction."

> These are called secondary structures, local patterns in the protein backbone

The corresponding figure is really messed up. The sequence of atoms in the amino acids are wrong, and the pairs of atoms which are hydrogen bonded are wrong. For example, it shows a hydrogen bond between two double-bonded oxygens, which don't have a hydrogen, and a hydrogen bond between two hydrogens, which would both have partial positive charge. The hydrogen bonds are suppose to go from the N-H to the O=C. See https://en.wikipedia.org/wiki/Beta_sheet#Hydrogen_bonding_pa...

> Given the same sequence, you get the same structure.

The structure may depend on environmental factors. For example, https://en.wikipedia.org/wiki/%CE%91-Lactalbumin "α-lactalbumin is a protein that regulates the production of lactose in the milk of almost all mammalian species ... A folding variant of human α-lactalbumin that may form in acidic environments such as the stomach, called HAMLET, probably induces apoptosis in tumor and immature cells."

There can also be post-translational modifications.

> The sequence contains all the instructions needed to fold into the correct shape.

Assuming you know the folding environment.

> Change the shape even slightly, and the protein stops working.

I don't know how to interpret this. Some proteins require changing their shape to work. Myosin - a muscle protein - changes it shape during its power stroke.

> Prions are misfolded proteins that can convert normal proteins into the misfolded form, spreading like an infection

Earlier the author wrote "It's deterministic (mostly, there are exceptions called intrinsically disordered proteins, but let's not go there)."

https://en.wikipedia.org/wiki/Prion says "Prions are a type of intrinsically disordered protein that continuously changes conformation unless bound to a specific partner, such as another protein."

So the author went there. :)

Either accept that proteins aren't always deterministically folded based on their sequence, or don't use prions as an example of misfolding.

I would assume so, but I didn't see any smoking guns in the text itself. But I'm also not familiar with the newest models here and their quirks.

Because it's what cool people do, so if you want to be cool you do it. They didn't realise the cool part was actually having the knowledge and actually writing the text.

There are many similar things where people just take shortcuts because they don't understand the interesting part is the process/skill not the final result. It probably has to do with external validation, reddit is full of "art" subs being polluted by these people, generative ai is even leaking into leather work, wood carving, lino cut, it's a cancer

I interpreted that section as alphafold not learning physics, but rather correlations within a constrained setting that a-priori correspond to physically sound inferences. It has a specific architecture that allows the model to make inferences that are more physically plausible than not, but not that it’s discovering actual, causally verifiable laws of nature (like what I’d assume are encoded into another non-ML approach to the folding problem for example).

I don't mind the style but the factual errors are not good. Like "How NVIDIA..." when it was done by DeepMind with TPUs.

Yes, counting memory bits in my PC doesn't fall into my job. But my question is a bit more fundamental.

Most of these numbers are hierarchical. I do count the memory modules, but not bits. I count apples, but not molecules in them. I try to count a few bright starts in the night sky, but not all stars in the galaxy. I try to stick to traditional non-GM food, which my ancestors ate, instead of counting protein molecules. I try to have childand grand kids, instead trying living eternally through great advances in science.