Curious as to what are some medically important examples of disordered proteins might be?
Transcription factors often are partially disordered, just to name one. A bunch of others here:
Readit Newsentee commented on AlphaFold won’t revolutionise drug discovery chemistryworld.com/opinio... · Posted by u/panabee
entee commented on AlphaFold reveals the structure of the protein universe deepmind.com/blog/alphafo... · Posted by u/MindGods
I can see the loops in these structures. I dont see the problem. It still added a structure to every embl page, and people are free to judge the predictions themselves. For all I care (ostensibly as the end customer of these structures) I don’t mind having a low confidence structure for any arbitrary protein at all. It’s only marginally less useful to actual biology than full on X-ray structures anyway.
> It’s only marginally less useful to actual biology than full on X-ray structures anyway.
I'm not sure what you're implying here. Are you saying both types of structures are useful, but not as useful as the hype suggests, or that an X-Ray Crystal (XRC) and low confidence structures are both very useful with the XRC being marginally more so?
An XRC structure is great, but it's a very (very) long way from getting me to a drug. Observe the long history of fully crystalized proteins still lacking a good drug. Or this piece on the general failure of purely structure guided efforts in drug discovery for COVID (https://www.science.org/content/blog-post/virtual-screening-...). I think this tech will certainly be helpful, but for most problems I don't see it being better than a slightly-more-than-marginal gain in our ability to find medicines.
Edit: To clarify, if the current state of the field is "given a well understood structure, I often still can't find a good medicine without doing a ton of screening experiments" then it's hard to see how much this helps us. I can also see several ways in which a less than accurate structure could be very misleading.
FWIW I can see a few ways in which it could be very useful for hypothesis generation too, but we're still talking pretty early stage basic science work with lots of caveats.
Source: PhD Biochemist and CEO of a biotech.
entee commented on Call for a Public Open Database of All Chemical Reactions pubs.acs.org/doi/pdf/10.1... · Posted by u/cbracketdash
Such a database would be hugely helpful across chemistry. Right now it’s extremely expensive to access databases like Reaxys or Scifinder, and they’re not usually programmatically searchable at scale. Some databases do exist based on the patent literature (https://depth-first.com/articles/2019/01/28/the-nextmove-pat...) but they’re not as well curated or complete. A pubchem like database for reactions would be really awesome.
I'm trying, really hard. But I have a disdain for not doing things 100 percent correct, and perfection is the enemy of progress. But especially when it comes to regulations that can get you in legal trouble. I just see all these things I need to get right, I need to learn about filing taxes and company accounts, hiring staff seems like an absolute minefield, and now you're also responsible for someone else's livelyhood! It's scary.
But I'm pretty close to launching "something" in the next few weeks. I'm doing a hardware startup, I already have multiple production-ready prototypes. Now I'm working on documentation, and some basic marketing material... but again all I can see is a hundred different things I still have left to do.
There's also the fact that the business would need to be quite successful to match my current, and rather hefty paycheck. Less money, less safety, but doing something I love... I hope it's worth it.
As a fellow perfectionist who has started a company, one thing that has helped me is realizing that most decisions are a lot more reversible than they appear. Even in the legal and financial domain, most things that you might obsess over are fixable if you make a mistake, and decent lawyers will tell you which ones you really have to avoid. Sometimes it'll cost you money and time, but the biggest cost is avoiding making decisions.
Always remember, no decision is a decision. Usually that's the worst choice because almost any decision, even a wrong one, at least moves the ball in some direction, allowing you to gather more information. The only guarantee in this game is that stasis will kill you, so bias towards action. When in doubt, try to evaluate "most probable bad outcome" which is different from "worst possible outcome".
Good luck!
entee commented on AI is changing chemical discovery thegradient.pub/how-ai-is... · Posted by u/andreyk
> Finally: every kid can draw up novel structures. Then: how do you actually fabricate these (in the case of real novel chemistry and not some building-block stuff). Noone has a clue!
I personally have a clue, and the entire field of organic chemistry has a clue, given enough time and money most reasonable structures can be synthesized (and QED+SAScore+etc and then human filter is often enough to weed out the problem compounds that will be unstable or hard to make). Actually even some of the state of the art synthesis prediction models are able to predict decent routes if the compounds are relatively simple [0]. The issue is that in silico activity/property prediction is often not reliable enough for the effort to design and execute a synthesis to be worth it, especially because as typically the molecules will get more dissimilar to known compounds with the given activity, the predictions will also often become less reliable. In the end, what would happen is that you just spend 3 months of your master student's time on a pharmacological dead end. Conversely, some of the "novel predictions" of ML pipelines includign de novo structure generation can be very close to known molecules, which makes the measured activity to be somewhat of a triviality.[1] For this reason, it makes sense to spend the budget on building block-based "make on demand" structures that will have 90% fulfillment, that will take 1-2 months from placed order to compound in hand and that will be significantly cheaper per compound, because you can iterate faster. Recent work around large scale docking has shown that this approach seems to work decently for well behaved systems.[2] On the other hand, some truly novel frameworks are not available via the building block approach, which can also be important for IP.
More fundamentally, of course you are correct, and I agree with you: having a lot of structures is in itself not that useful. Getting closer to physically more meaningful and fundamental processes and speeding them up to the extent possible can generate way more transparent reliable activity and novelty.
[0] https://www.sciencedirect.com/science/article/pii/S245192941... [1] http://www.drugdiscovery.net/2019/09/03/so-did-ai-just-disco... [2] https://www.nature.com/articles/s41586-021-04175-x.pdf
There's a lot that can be learned with building-block based experiments. If you do a building block based experiment then train a model, then predict new compounds, the models do generalize meaningfully outside the original set of building blocks into other sets of building blocks (including variations on different ways of linking the building blocks). Granted that's not the "fully novel scaffold" test, however it suggests that there should be some positive predictive value on novel scaffolds.
We've done work in this area and will be publishing some results later in the year.
entee commented on AI is changing chemical discovery thegradient.pub/how-ai-is... · Posted by u/andreyk
Shameless self promotion: I wrote one of the more cited papers in the field [0], back in 2016.
A key challenge: very few labs have enough data.
Something I view as a key insight: a lot of labs are doing absurdly labor intensive exploratory synthesis without clear hypotheses guiding their work. One of our more useful tasks turned out to be interactively helping scientists refine their experiments before running them.
Another was helping scientists develop hypotheses for _why_ reactions were occuring, because they hadn't been able to build principled models that predicted which properties were predictive of reaction formation.
Going all the way to synthesis is nice, but there's a lot of lower hanging fruit involved in making scientists more effective.
This is true. Getting datasets with the necessary quality and scale for molecular ML is hard and uncommon. Experimental design is also a huge value add, especially given the enormous search space (estimates suggest there are more possible drug-like structures than there are stars in the universe). The challenge is figuring out how to do computational work in a tight marriage with the lab work to support and rapidly explore the hypotheses generated by the computational predictions. Getting compute and lab to mesh productively is hard. Teams and projects have to be designed to do so from the start to derive maximum benefit.
Also shameless plug: I started a company to do just that, anchored to generating custom million-to-billion point datasets and using ML to interpret and design new experiments at scale.
entee commented on Rendering on the Apple M1 Max Chip blog.yiningkarlli.com/202... · Posted by u/bhouston
I thought the observation was always that instruction set was not such a big difference in high performance CPUs next to manufacturing technology which was by far the first order effect. It would be expected for a high performance ARM CPU to reach roughly the same performance in that case (AMD is 1 generation behind here, I think Intel is 2).
Is there really "chipheads" who are predicting ARM ISA to buck this trend and start pulling ahead at equivalent technology nodes? By what mechanism do they believe this will happen, do you know?
Not a chiphead, but saw this in the article that might be a reason ARM is better for this kind of thing:
"The theory goes that arm64’s fixed instruction length and relatively simple instructions make implementing extremely wide decoding and execution far more practical for Apple, compared with what Intel and AMD have to do in order to decode x86-64’s variable length, often complex compound instructions."
Not sure it's true, not an expert. But it doesn't sound wrong!
See jarenmf's comment. Basically the CO2 is catalyzed into Methane using an 'inorganic catalyst', there is this paper[1] which uses a ruthenium catalyst at 400 degrees C to do it.
Then the methane is consumed by some genetically modified bacteria (probably e.coli) first to convert it to an intermediate product, and then with a second organism to convert it into starch.
In the article they talk about genetically modified enzymes and I've actually jumped ahead to the using genetically modified bacteria here[2]. I think that is a safe assumption as a number of bio-reactors do exactly this.
What is somewhat interesting to me is that livestock generates a lot of methane, if you could harvest it and convert it back into starch to feed the livestock you could increase the efficiency of raising livestock farming.
[1] https://phys.org/news/2020-02-method-carbon-dioxide-methane-...
[2] The article hasn't appeared in sci-hub yet :-)
Most of the methane comes from cattle belching, so it's basically impossible to harvest at scale. The lagoons of manure also do, and that can be harvested though it's hard.
https://climate.nasa.gov/faq/33/which-is-a-bigger-methane-so...
From the paper in question[1]
"carbon dioxide is reduced to methanol by an inorganic catalyst and then converted by enzymes first to three and six carbon sugar units and then to polymeric starch. This artificial starch anabolic pathway relies on engineered recombinant enzymes from many different source organisms and can be tuned to produce amylose or amylopectin at excellent rates and efficiencies relative to other synthetic carbon fixation systems—and, depending on the metric"
The catalyst is the part I'm most curious about. Carbon capture is a hard problem at scale. Once you have methanol, I'm not terribly surprised that you can convert it to other things enzymatically. Hard to evaluate without full text of paper though.
I don't know of any other driving assist system that get anywhere close to this. Every time I tried fancy rental cars (BMWs, Audis, Mercedes) with the latest and greatest driver assist, it's feels like a joke.
What am I missing?
Tesla is uniquely risk tolerant for better or worse. You also don’t hear about people getting into accidents in a BMW on self driving because they don’t make the same claims and have tons of safeguards.