Uniprot fills a pretty big niche - proteins. Your body is literally made out of proteins. They carry out most of the work and chemical reactions that amount to what we call life, and almost all drugs work by modulating some protein target in some way. UniprotIDs are the canonical identifiers that computational biologists use for proteins.

But they are just one knowledge base. There are others. Each with their own focus area. And they are all associated with prominent bioNLP / biomedical AI research labs and employ human SME curators.

> 5-10 avg salary non-star ML FTEs likely produce very mediocre and likely non usable results

LOL!

> But they are just one knowledge base. There are others.

So, is your claim that all topics/niches/verticals/steps in pharma development and productions are covered by all those databases with absolute quality and perfect UI/workflow? I follow some studies about trials meta-analysis, and my impression is that end result is mainly produced by manual work of some low paid postdocs, which makes them not trustworthy.

> LOL!

I actually claim non-trivial expertise in this area (facts extraction from untrivial niche documents), and my observations is that even SOTA (aka results from top researchers) are hardly useable in real life, because this area is very hard. Could you support your "LOL!" with any references I could check?..

The parent is right, saying a database about proteins 'looks like one narrow niche knowledge base.' in this context is just nonsense.

in which context, and why your bold statements should be trusted blindly?

TL;DR: I explain what proteins are and why they're important, show some biological "flowcharts" , and end up with one "function definition" from the "source code" that makes you you, and has to do with you eating and breathing.

Long:

This is the bit that makes biology awesome to me, so excuse me for the small essay ;-)

Proteins are basically extremely advanced nanomachines which work together in larger systems to ultimately form a cell. Having a listing of all the proteins in a cell (the sum of the parts) is insufficient to grok the whole, but it's pretty darn important. The abilities and limitations determine and constrain what a cell can do, and ultimately influence what organisms and ecosystems are and are not capable of. Which is a big chunk of the science of biology.

Uniprot lists many/all of the genes/proteins that have been decoded so far. It's a bit odd to call that a "niche" in the context of the field of biology.

I'm not going to discourage you though: Proteins and protein systems are pretty darn awesome!

Example:

(using KEGG rather than uniprot, since it's got graphical maps, which is handy to get an intuition)

For instance, if you want to know why you need to eat and why you need to breathe (flowchart for what your cells do with starch and oxygen):

* https://www.genome.jp/pathway/map00500 start by finding starch on this map (gets split into glucose)

* https://www.genome.jp/pathway/map00010 which gets broken down into 2* pyruvate

* https://www.genome.jp/pathway/map00020 which gets processed

* https://www.genome.jp/pathway/map00190 and ultimately "burned" with oxygen.

Each step is a 'chemical reaction catalyzed by proteins'[1] (in the rectangles). You can dig in deeper to find your actual source code: Say we click on a random step (in this case near the top of glycolysis on map 00010)

* https://www.genome.jp/entry/K01810+K06859+K13810+K15916+5.3....

At the bottom you can find the gene listed for Homo Sapiens (HSA)

* https://www.genome.jp/entry/hsa:2821

And this lists the amino-acid (AA) sequence for the protein, and the nucleotide (NT) sequence found in humans. Since this is highly preserved functionality, that's probably (almost) exactly the source code that you have in each of your cells.

KEGG is nice to get an overview of some of the pathways that are fully understood with the maps.

[1] calling it a "chemical reaction" is sort of underselling many proteins. Proteins can have moving parts and can work together. I prefer to think of them as sophisticated nanomachines.

> Uniprot lists many/all of the genes/proteins that have been decoded so far. It's a bit odd to call that a "niche" in the context of the field of biology.

I actually checked uniprot, yes, it lists proteins (probably most of them), but ontology is raither narrow, it has few dozens properties, you can't for example query that DB with question: give me diseases which can be attributed to broken pathways synthesizing protein X, you would need to do a lot of manual work and check external databases of uncertain quality.

Another question is quality of that dataset, why it is so obvious that all those millions of pathways for hundreds thousands pathways researched and described with 100% accuracy?

> you can't for example query that DB with question: give me diseases which can be attributed to broken pathways synthesizing protein X, you would need to do a lot of manual work and check external databases of uncertain quality.

nod

Uniprot is more useful if you're looking for the actual "bare metal" NN and AA sequences. Which is rather important in its own right, obviously: Sooner or later you DO need the actual sequences if you're going to do something with them in real life.

But uniprot doesn't -itself- give you an understanding of what that code is then doing.

I already gave you a real life example with the uniprot reference. Here is another flagship knowledge base that heavily leverages NLP extraction.[0] Here is another one that gets used in what seems like every network biology article.[1]

Meta analyses? Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery.

Also the most valuable knowledge is not in any publication. If you are reading about it in an article then you are already 2-3 years too late.

0. https://geneontology.org

1. https://string-db.org/

> that heavily leverages NLP extraction

I search https://www.google.com/search?q=site%3Ageneontology.org+nlp+... and don't see anything meaningful.

> Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery.

my opinion is that it has to be top level tool in modern science which could easily sort out lots of bs and contradictions in reported results.

> this looks like one narrow niche knowledge base. I am not expert in this domain, but there are probably some other use cases not covered by existing offerings.

UniProt is not niche. It contains curated information about all proteins across numerous domains.

yes, proteins is a niche in grand scheme of things

The GP was talking about pharma. Proteins are not niche in pharma. Everything else may be niche in that domain, but proteins make up more than 95% of the targets of the pharmaceutical industry.

and an increasing number of drugs- in the "old days" it was almost entirely small molecules, but they are starting to peter out (both because the low-hanging fruit has already been plucked, and also, small molecules are a usually a terrible way to modulate biological activity in specific ways.

> The GP was talking about pharma.

post is not about pharma specifically, but about general bio-medical literature, including genes, diseases, symptoms, etc.

> proteins make up more than 95% of the targets of the pharmaceutical industry.

do you have references to support this? Some google search says it is 280B market out of 1.5T total pharma market: https://www.alliedmarketresearch.com/protein-therapeutics-ma... https://www.statista.com/topics/1764/global-pharmaceutical-i...

Also, it is likely important steps how to research and produce protein drugs, but the end target is to cure diseases, so you need lots of additional data about diseases of all types, symptoms, pathways, trials, etc.

This isn't a "do you have references" situation. You're wasting folks time. Nearly all drugs target proteins, with a few that target DNA or RNA.

You can just ignore my comments and walk away? To me you just another "internet expert" which I am not sure why should I blindly trust.

Your sources are talking about the ratio of small molecule vs. large molecule drugs. Even if you're developing small molecule drugs you are likely targeting some aspect of protein signaling/gene expression.

People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google.

> People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google.

its all about how you define word "niche", for google, main revenue stream is supported by several pillars: search tech, infra tech, ads tech, ecosystem+network effect, human management. You remove one pillar, and everything is destroyed, so one can say ads is one of the niches in their food chain. I suspect with proteins it is about the same.

> in the context of pharma

there is no context of pharma. Post is about more broad bio-medical publications.

> Word of advice to all those who are chomping at the bit to disrupt pharma with AI.

Literally the first line in the comment that started this thread.

Sure, now let's read the post?

I read the post before I made any criticism of your comments. We're talking on a thread within the larger context of comments on the post. But more importantly, if you read the post, you will see there is a theme of industrial biochemistry (IE, pharma and biotech) running through it, because pharma/biotech is the primary consumer of these products, and the vast majority of the revenue stream.

> there is a theme of industrial biochemistry

that's one of the themes (and you are already working hard to stretch drugs pharma to "biochemistry"), if you can't see other themes in his examples and screenshots, I think this discussion is not interesting to me.

I'd say it's less like pillars and more like (emergence) layers, with proteins being a pretty important layer .

I'm now not entirely sure what your experience is with bio sciences. You're definitely coming at it from an odd angle though!

I didn't claim expertise, that's why I say "it looks like", "I suspect",

Well, maybe find some time and dive in a bit and see what can be found?

You never know, maybe you'll end up contributing to our understanding of life, maybe (indirectly) even save a few lives!

I am working on the service which potentially can answer questions like in this comment: https://news.ycombinator.com/item?id=38109294

life science is one of potential applications if there is an interest and money.

So the pathway to synthesize every protein is +/- the same: That's gene transcription[1] and translation[2]. If that's broken, you're in big trouble!

But if you mean in general if you're capable of looking at protein pathways where each protein catalyses a step in the pathway, that's definitely interesting. If a certain person has a flawed gene coding for protein X, that could indeed cause a problem.

To find valid answers, you might need to eg. track nodes and states in a graph, to figure all the consequences of a break. Not all types of storage systems/engines are equally good at that.

[1] https://en.wikipedia.org/wiki/Transcription_(biology)

[2] https://en.wikipedia.org/wiki/Translation_(biology)

> Not all types of storage systems/engines are equally good at that.

Yes, I built system which traverses paths in graphs with 1B nodes and 10B links in 1h on affordable server. But that's only one part of the puzzle.

Neat!

The first Google search link you've provided is focused on proteins as an active ingredient (like an antibody), not the targets.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314433/ Check out this article for example.

So, that link says they discovered 1.5k FDA approved drugs which target proteins, while FDA has total 19k drugs approved: https://www.fda.gov/media/115824/download#:~:text=FDA%20regu....

Hi there. Apologies for any confusion. I was clarifying the intent of the first comment in this thread. I will add some impressionistic comments below in case it helps your future service.

Even when a drug target is unknown, or remains mysterious, very high chances are that the target is a protein. DNA or RNA as targets are niche (DNA is often avoided as a target on purpose and it’s hard to be specific to it without DNA-like material, and RNA is still hard to target effectively, though things are improving). There is not much else of use in the cells (lipids, sugars, cofactors, and metabolites, some examples of which have been targeted by a couple drugs each over the long history of trials, often unintentionally.

Small molecules are an excellent modality for an eventual approved therapy. They almost always (so far) targets a protein. They are hard to design but when they are done well they expose the target to something nature hasn’t seen before in order to get a desired effect. Sometimes people don’t care about the target itself (think recreational drugs, or phenotypic drug discovery), but the target typically remains a protein.

Proteins make up the machinery of the cell. You jam or modify them to achieve desired effects.

Also, and totally minor: there are nowhere close to 19k different approved small molecules. The same drug can be included in multiple products or formulations bringing that number you mentioned to 19k marketed products. Each generic formulation of iboprofen increases the latter count by 1. Counting all the marketed products of pure orange juice may add up to a large number but it is still one ingredient.

Yes, I compare drugs with drugs, not molecules with drugs.

No. The analysis in the paper above referred to molecules vs the snippet in the fda referred to products. More generally, other than marketing materials to doctors or patients or very rare exceptions talking about formulations etc, the scientific literature refers to the molecular entity as a drug not the particular named/branded product. I hope that the tool you are building will not mix up such concepts.

The link you posted found "1,578 US FDA-approved drugs"(exact citation).

Yes that is the standard language in the field. It does not refer to the number of marketed products. Look up the number of novel drugs approved by the FDA per year. It only recently exceeded 40 per year, and the FDA has not existed for very long.

if we consider that you are not making things up(which I am very not confident), then your link is useless in this discussion, because it doesn't give a sense how many non-protein targeting drugs were approved by FDA.