We Made One Gram Of Remdesivir
acsh.org
acsh.org
Our body, or really, all biological processes can synthesize incredibly complicated molecules that can take human chemists a huge amount of effort to synthesize. It really is amazing how awesome our body is.
†: My description here is a dumbed down description. For a more precise description see section 2 of Arguments in favour of remdesivir for treating SARS-CoV-2 infections, Wen-Chien Ko et al, https://www.sciencedirect.com/science/article/pii/S092485792...
What I’m curious about is why this huge group attached to the adenosine-like group is needed. It seems to be rather complex for being a shoe to be thrown into cellular gear. Do you have an idea or pointer into the mode of action of this group?
So then you add different greasy groups to new compounds and screen those. So will be worse, but sometimes some will be better. Then you look at the better ones, like having a nitrile group off the 1' position of the ribose and maybe that started as an amine (I'm making shit up here) and they decided to make it stick further out (IDK).
Anyway, I did some quick looking at it seems like remdesivir is a prodrug that gets modified by other enzymes to become triphosphorylated and then incorporated into the RNA genome of the virus (https://www.nature.com/articles/nature17180/figures/1). So they got super lucky finding it! Check out that paper for the story.
The adenosine bit is attached to a five carbon sugar (pentagon with O at top) which is identical to the sugar it would be attached to in RNA. The next thing along is a phosphate with some oxygens double bonded to it, which is part of the "backbone" of DNA. The stuff attached to that phosphate is nothing like DNA or RNA.
Hope this makes sense and provides a little insight for you :)
It's all part of ADMET (absorption, distribution, metabolism, excretion and toxicity) optimization. Pharmacokinetics is an important subject, and that's why you can use the "XY shoved activity in vitro" papers only as starting points.
So, putting the three together, would it be possible to use actual biosynthesis for designed molecules by basically writing your own DNA/RNA and inserting it into a cell?
(Or is this already what's being done?)
But if I could make a guess I'd say that it in theory it might be relatively straightforward or easy to do but it's probably a lot harder in practice. That's often the case anyways.
Long answer: No. It would be an incredibly complex undertaking.
The entire thing is so unimaginably complex. For example, for a lot proteins that are catalysts (aka enzymes) the actual catalytic part is a metal ion and the protein mostly provides scaffolding. A nucleotide sequence alone doesn't directly tell you what ion is needed. In some cases, multiple ions can fit, but only one actually results in the protein work. This the basis for how a lot of toxic metal exert their toxic effects.
It's also not as simple as a nucleotide sequence codes for a protein and that's it. Proteins fold into their final shape from the chain of amino acids that DNA encodes. Protein folding in general is a hard problem. Biological proteins may have other proteins (called chperonins) that help them fold into their proper configuration. Then proteins may also be modified after they've folded (again by other proteins). Some proteins are made up of multiple sub-units as well.
> Proteins fold into their final shape from the chain of amino acids that DNA encodes. Protein folding in general is a hard problem.
My hope was that we had made progress in exactly that domain. Yes, the relationship between a nucleotide sequence and the resulting protein is extremely complex, but my impression was that we have tools to simulate the folding process for a given chain of amino acids (Folding@Home comes to mind).
So I was imagining a brute-force like process, where you (somehow) start with some candidate sequences, simulate how they would fold and use the sequence that comes closest to the molecule you wanted to have in the first place. Of course this only works if your target molecule can be assembled out of amino acids.
From 2009: https://www.nytimes.com/2009/02/07/business/07goatdrug.html
If you want an easily accessible jawdropping intro David Baker's youtube briefs here are pretty cool:
https://blogs.sciencemag.org/pipeline/archives/2010/10/06/ch...
https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002...
> The adenosine analog NITD008 has been reported to directly inhibit the recombinant RNA-dependent RNA polymerase of the dengue virus by terminating its RNA chain synthesis. This interaction suppresses peak viremia and rise in cytokines and prevents lethality in infected animals, raising the possibility of a new treatment for this flavivirus.
Absolute gibberish to someone with limited knowledge of biology.
Rigorously precise language (e.g. "jargon") is almost never needed for a top-line description of a thing, and I think should be discouraged, in general. Yes, highly technical descriptions targeted toward professionals and academics in the field have their place in wikipedia, but ought to be relegated to periodic additions to the body of the article. That's just my opinion, of course, but as such I think its quite correct and admirably justified!
random example: take a look at the Wikipedia article for "currying". [0] while quite accessible to someone with a math/cs background, this would be pretty much unintelligible to someone who wasn't familiar with the notion of a mathematical function. perhaps it could be rewritten to be even more accessible, but what would be the point of explaining currying to someone who doesn't know about functions and arguments in the first place? brittannica doesn't even cover this topic.
I know Wikipedia isn't supposed to be a textbook, but I'd argue that having a more accessible first paragraph or summary section on every topic could help all uses:
- Specialists would more easily be able to refresh their memories of topics they use infrequently before diving into the details.
- People in neighboring specialties can more easily branch out.
- Informed laypeople (e.g. experts in other industries) could more easily find new ideas for cross-pollination into their own field.
As one random example I have recently found application for an algorithm mainly reserved for use in geophysics and cartography to audio signal processing, but learning and applying it took way more research than it really should have.
I'm curious how you would rewrite that first paragraph then:
"In mathematics and computer science, currying is the technique of translating the evaluation of a function that takes multiple arguments into evaluating a sequence of functions, each with a single argument. For example, a function that takes two arguments, one from X and one from Y, and produces outputs in Z, by currying is translated into a function that takes a single argument from X and produces as outputs functions from Y to Z. Currying is related to, but not the same as, partial application."
BTW I don't like that paragraph, it looks to me that it completely "misses the point" because as far as I understand currying is not about the sets but about the arguments. If it would be about the sets it would be just "a rewrite to a function that accepts the subset of the previous input set" and it's not about that.
simple wikipedia does take your approach though:
> Currying is a technique used in mathematics and computer science that consists of changing a function that takes several arguments into a number of functions that each take one argument. Mathematicians Moses Schönfinkel and Gottlob Frege laid the groundwork for this technique, which is named after Haskell Brooks Curry. Currying is used in Lambda calculus. Some programming languages, such as ML and Haskell say that functions can only have one argument. [0]
that's actually the entire article. not sure how useful it is, but at least they tried.
If you're not convinced, even professional scientists themselves regularly publish and read "review papers" which are pretty much papers doing no research other than summariing and simplifying the current state of the research in their field.
I don't see this as being always true. For example, scientific papers are written in such a way that normally you need a great deal of understanding in a field to read them. Making them accessible would mean either removing or explaining all domain specific knowledge in each paper. Expecting the reader to acquire the knowledge elsewhere and then read the paper with the knowledge makes a group of papers more accessible than if each paper did either of the options making them simple.
In the same way, the wikipedia page on integration by parts does a better job of explaining what it means than if it took the time to explain what a function is, what a variable is, what an integral is, what a product is, what an antiderivative is, or what the common notation it uses. I bring these up because these are all assumptions made before you even get to the table of contents of the article. If the article was simple enough that someone who didn't know any of those concepts could understand the same information presented in the first paragraph, you would have a significant introduction to mathematics that would slow down those who have some basic calculus knowledge seeking to refresh or get a summary of what integration by parts is.
It quickly helps forging an intuition of the new concept, using phrases like "Thus integration by parts may be thought of as deriving the area of the blue region from the area of rectangles and that of the red region." which are not very mathematicallish.
All the while, it's a good article for maths people.
Once again, in a great article, what should be simple and explained is not prior knowledge, but the new concept introduced.
I like Wikipedia as it is, let's not make it into IdiocracyPedia for the sake of accessibility... if you're not lazy and you're willing to put some focus and time into your research you'll be able to understand quite unfamiliar subjects from Wikipedia, there's nothing blocking you, as opposed to eg. lots of academic articles that may contain un-google-able jargon and unexplained/unmentioned domain specific assumptions.
> Wikipedia is an international encyclopedia. People who read Wikipedia have different backgrounds, education and opinions. Make your article accessible and understandable for as many readers as possible. Assume readers are reading the article to learn. It is possible that the reader knows nothing about the subject, so the article needs to explain the subject fully.
Wikipedia isn't magical. If you don't care enough to edit it, it won't include the information you'd like to see.
> inhibit the [...] RNA polymerase of the [..] virus
If it would just simply stop RNA synthesis (eg. inhibit any RNA-polymerase) or if it would actually break RNA in general, it would kill HUMANS just as well!
The point of antiviral compounds is to selectively inhibit/kill mechanisms/components of the virus and not the human host... there's hundreds of thousands of antiviral and antibiotic compounds that are not very useful because they'd kill humans just as well or give them horrible cancers or god knows what else...
As the saying goes... "Everything should be made as simple as possible, but not simpler."
Any insight on how it manages not to harm non-virus stuff?
(Not a biologist. Also you're using a lot of italics and it harms the readability at least for me.)
But great professionalism can also be developed without as much cross-functionality between subdisciplines.
Like many things, detailed efforts unique to the subprofession can be the most essential.
Disclaimer: I'm terrible at organic chemistry.
As with software engineering, you develop a general sense (I didn't) of what might work and what probably won't. You aren't coming at it blind and reinventing the wheel every time. You learn to recognize patterns in chemical structures and reason about how they will interact under various conditions based on that. You do electron pushing in your head without giving it much thought, similar to a programmer reasoning about object lifetimes or dataflow in an application.
As to -100 C or +5 atm, that's the easy part. You alter environmental conditions when what you're working with is too reactive, or not reactive enough, or you have some other general problem. It's roughly analogous to determining the minimum amount of RAM the machine hosting your production database requires.
That part, at least in theory, is easy to understand.
Chemical reactions are typically of the form:
ingredients + energy -> products + byproducts
If the energy is on the left-hand side, making the environment warm makes the reaction go faster. If energy comes out (so it's on the right-hand side), making it cooler is better. You also need to control the temperature to be in a range where both the ingredients and products can survive.
As for the pressure, if the products and by-products have more total volume than the ingredients, low pressure is good. If it's the other way round, high pressure encourages the reaction.
The more complicated part is that if multiple reactions can happen with the same ingredients, or if the products can do further, unwanted reactions. Then you have to balance out the parameters to encourage just the reaction you want, and to discourage all the others.
Back then, you had a certain number of mechanical properties in mind as well as well-known "parts" (gears, linkages, etc) that you could predict the properties of very well - and the task was then to assemble them into larger mechanisms that did what you wanted them to do.
Seems to me, the intuition here could be similar - except the number of dimensions in which parts can interact is larger, the "clockworks" are orders of magnitude more complex - and your tools are much more coarse, so mostly, even if you know what you need to build, the building itself can only be done indirectly.
I have a bachelor in chemistry and ochem was one of the most painful classes based on the amount of things you must know by heart for synthesis
Plus, WSB is more of a meme than real education.
Things that might help pique your interest:
- For both academic and practical problems: http://quant.stackexchange.com/
- For keeping tabs on new ideas in the blogosphere (generally average, occasionally something useful): https://quantocracy.com/
- Useful information on getting started in the field: http://quantstart.com/articles
Outside of the academic literature, it is quite an insular field, so you have to work to find interesting ideas. If you can make money, you probably won’t be sharing much with anyone. A notable exception might be Rob Carver’s blog (https://qoppac.blogspot.com/).
From a CS background, a simple path to see if you might enjoy “quant investing” is to read some old papers from e.g. AQR about factor investing (Momentum, Value) and try to implement it with Interactive Brokers or similar. You should be able to roll something together with very little capital.
On the “quant trading” side of things, try the Avellaneda paper for statistical arbitrage, do some digging about RenTech, read the LTCM book, etc etc. Not something you can do without a lot of infrastructure behind you, but there’s enough information around to work out if it’s interesting.
If you're comfortable, could you please share how you made the transition to quant from a programming/SWE background?
1) Quant Developer: Trading infrastructure, working on research platforms, taking algorithms created by researchers & deploying into production. Wouldn't want to do this at a bank, but at a hedge fund or proprietary trading firm it'd be fun. Compensation is good, lower variability, but somewhat more limited upside. DE Shaw, 2 sigma, Jane Street, etc.
2) Quant Researcher: A high quality quant masters degree at a minimum (think Baruch MFE), but often a PhD. Working on systematic trading research, market making algorithms, optimal hedging for large books of derivatives, etc. Usually a decent salary, but total compensation varies wildly based on performance.
3) Trader: More and more similar to #2 these days, but more focus on execution of strategies and real-time action rather than research, and more directly responsible for PnL. Optiver, IMC, SIG, etc. A quant masters degree probably isn't required to get a foot in the door, but I think it would help in the long term (e.g. helps build more rigorous understandings of why what you're doing might be working).
For biochemistry check out r/labrats
1). So many syntheses have horrible yields just like this one. You’d start with grams of material to end up with micrograms. I loved solving these problems as an undergrad in books, but reality was far different. You don’t think much about side products until you start doing novel chemistry.
2). So much trial and error. There were happy go lucky chemists that fell into projects that were smooth as butter, while brilliant chemists would toil 12 hour days to try and something to write up as a thesis. I was neither brilliant nor lucky and took 4 different projects over two years before finally landing on something marginally MS worthy. They need a journal of failed chemistry because only the working stuff gets published. So many failures could be logged so I didn’t waste my time doing non-working or poor yielding reactions.
3). Suspicious results in journals. I would read about a reaction and someone would put a 75% yield as their result and I could barely get 20. I always thought I was just bad, but a really smart chemist challenged me one day and tried to do it himself and couldn’t do much better. He tried it 30 different ways over the year as he did other stuff. He never could get a good yield. We talked to our advisor and we wanted to challenge the result, but the advisor didn’t want to start trouble. It was past the time I decided to leave with a masters, but made me feel a little better about my lousy abilities. No one could ever possibly doublecheck every result from every publication anyways.
All this said, there are some brilliant and patient scientists out there that drive the field forward. Just a few rough around the edge items I’d love to see change.
Having gone through a master's myself, I think when we pushed two generations of children into college, we generally lowered the bar - across the board, effectively. And that's related to what's happening in the US today.
I think most instrumentation scientists are sympathetic to this. There are also lots of instrumentation jobs that don't require stupid numbers of publications because it's not practical to find candidates. There are relatively few good hardware people in the sciences (especially fields where you can't get a mech/electrical engineer).
I suggest finding some conferences to start. They're a good venue for telling people what you did. There are also journals specifically for building stuff, SPIE has a lot for astrophysics, for example.
The engineering really is the crux of the problem. I personally find it very interesting and I feel like an oddity in my department for that. Most other physicists kind of look down on the "e-word" to the point that my advisors made me change references to "engineering challenges" into "experimental challenges" for a conference talk I gave. I'm in a very equipment heavy field too which is why I find it so strange.
What's frustrating though is that sometimes it feels like I'm only doing engineering with no physics in the mix. Hopefully thay will change over the next year though.
In astronomy it's called instrumentation. Astro has relatively sane nomenclature. You have observational (looking at stuff and gathering data), theoretical (theory and simulations) and instrumentation (building stuff). Everyone in the field understands those terms. That said, instrumentation spans everything from construction, design, calibration/characterisation, etc.
In other branches, as the OP pointed out it may be "experimental" physics. Problem with that term is it's extremely vague. That's essentially anything which doesn't involve theory or simulation - lab work. That doesn't necessarily mean you actually build anything though. I was surprised at how little we got taught about instrumentation during my physics degree. Of course we had labs, but it was more about using kit than how it was built. It's almost as if it's someone else's problem, even though that's not how research actually works.
The irony of physicists sneering at engineers is that most labs employ a bunch of them who do most of the actual design and build work. They tend to be less focused on publication, but they are absolutely critical employees. The sorts of people who've spent 20-30 years working on some uber niche detector tech and know more than the people writing the papers for sure. A good chunk have physics PhDs.
When you get to big money stuff like particle accelerators and telescopes, a lot of this is contracted out. Physicists and engineers are responsible for designing the spec, but it's less tinkering in a lab with hardware. In industry it's mostly called engineering (e.g. optical, mechanical, electrical), but I've seen engineering physics too. That tends to be in companies that build very niche equipment specifically for physics research.
There is one professional engineer in the lab my group is a part of, but they primarily work on the bigger projects. For this, I have to do all of the CAD work and talking to the machinists. I also machined a lot of the parts myself due to time constraints.
Pro-Tip: Want a 'quick' nobel? Do optics for ~3/4 years. Then never touch it again. Making new kinds of microscopes is crazy useful and high impact, but you either get lucky or you waste 45 years in a lab. Try it out for a bit, throw a few on red, then walk away from the table.
I agree that better sensor tech is also very useful. It is the end of the line for the optical path, after all. Any way you can get photons picked up better is great!
But the actual optics, the mirrors, the lenses, the fibers, the filters, the E-O waves, etc. That's where all the jazz is. I'm not kidding when I say it's an easy nobel. STED was just putting the right filter in the right place. DIC was just using a 1/4 plate just off the focus. PALM/STORM is just using a specific dye and a fast lamp. Blue LEDS are just a bit of chem. Optogenetics is just the right slime out of just the right pool.
Now, all those things built up on a LOT of other work, but it's not terribly difficult stuff to do. The processes are very straightforward. I mean Hell built STED in his living room out of cardboard boxes, literally.
But, you can also toil away on these projects for decades, tweaking this, isolating that.
Optics is absurdly touchy.
When we measured it, the material was actually terrible and we don't understand why it's that way outside of some speculation. Unfortunately it also feels like our lab doesn't have the type of expertise to explain why it's so bad either at least not without years of learning.
My advisor's explanation on publishing was that if we had great results that followed our initial theory of what's going on then we could write a paper and nobody would question it. But, now that we contradict theory the threshold of evidence is much higher and he doesn't want to go ahead with it.
We trust our data, but he's concerned about getting it through peer review. For what it's worth we did publish it in a conference proceeding, but it doesn't have the same weight as a journal article.
Isn't that sort of the most important thing to publish? Something that contradicts theory means something new. Seems like all the more reason to pursue it, no?
Publishing something that goes against the grain means a lot more scrutiny on your work and bigger humiliation for any mistakes made. If you're publishing against the grain, you have to double triple check every single detail to make sure it's all iron-clad. This costs a lot of additional time and money. When publishing research that preaches to the choir, no such considerations are required.
In an ideal world, publishing against the grain should be encouraged and there should be no extra reputational penalty for getting caught with errors in that type of research (heaps of errors go unnoticed in mainstream papers, even popular ones). But that's not how it works sadly.
I feel like anonymity could possibly go a long way for this.
Yes, it is important to publish results that violate established theories, but if there's already a lot of validation of those theories through experimentation, you're going to have legitimate pushback, especially if your method of experimentation isn't the most sophisticated.
If the result of your publication is a theoretical breakthrough, then that's great, but unless you're in the top tier within that discipline, the likely outcome is that your research turns out to have a big flaw which you missed, and you invite a bunch of people to point out to the world that you can't do science properly...
[0] https://www.sciencemag.org/news/2012/06/once-again-physicist...
We don't understand why our data doesn't line up with theory and this isn't some super well funded experiment like the ones at the LHC with like a million grad. students. This is just me in the lab alone with the hundreds of subtle ways the experiment could have gone wrong or needed a slight modification to the theory. It would take us a lot of effort to distinguish an experiment gone wrong from an interesting new development and that's not always worth it. Especially for such a niche area of research like I'm in.
Also, we did publish our data. We just did it in a conference proceeding with a lot of caveats attached to it instead of in a big journal article. That's the aspect that I was complaining about, but I do understand my advisor's decision.
I have to strongly disagree that science is broken here.
For the purpose of producing knowledge. It does other things.
There are real issues in academia, but I disagree that what happened here is wrong. We did publish our data, we just didn't do it in a journal article. In my fields, this is actually fairly typical and journals are less of the norm unless you have a big result.
The whole problem is that extraordinary claims require extraordinary evidence. Sometimes when you don't have much funding and there's only a few grad. students in the lab, that evidence is no longer worth the time to collect.
See the comments below about the "neutrinos travel faster than the speed of light" paper. There is a real danger in publishing theory-breaking results when you don't have 100% confidence in them.
To be precise, I already think science is broken, and your story seemed to confirm it.
Obviously, that "this is actually fairly typical" is not a counterargument to a systemic belief.
That said, it's entirely possible I jumped the gun here.
I take the reproducibility crisis to be a strong indicator that something is Very Wrong with science. I'm also convinced that the modern peer review system is fundamentally broken.
However, my take-away was that the successful researchers were the ones who could take any decent experiment and figure out what was publishable about it, or at least steer it into a publishable direction.
In a different but related world, clandestine chemists share failures more often than they do their successes, at least in the communities I was a part of a very long time ago.
I'd wager this is because our substrates, reagents and solvents are such a pain in the arse to get compared to an actual lab, that wasting any of them is a no-go if it can be avoided.
Related to that, but we reused solvents and recycled material a lot more than I did doing my B.Sci in Chemistry, for the same reasons!
I got similar stories about how to (not..) grow mammalian cells in vats.
If it makes you feel any better (I 'quit' at the start of my bioinformatics PhD), there's a high chance that their projects went smooth as butter because they were also happy-go-lucky about double-checking their results. See for example that 75% yield paper you mentioned.
I don't know organic chemistry, but in my field the authors are often willing to discuss their results, especially if it might mean more citations for them.
Collider physics. I'm guessing that a lot of the less applied sciences are the same way: we're looking for fundamental laws so it's more about convincing people that your result is valid than about trying to make money off of some application.
It's very hit or miss of course: there are still some groups that act very secretive and some that are more open.
I've had to push my colleagues to cite a number of non-traditional sources: arXiv, github, and zenodo for example. Fortunately most of them agree that citations are cheap and that giving more people credit is generally a good thing.
One thing that helps is publicly stating how you want your research cited. If you don't have a peer reviewed publication in the pipeline, tell people how to cite your work on a blog or your github page or somewhere. Most people default to peer-reviewed journals for citations and get confused when one doesn't exist, so an explicit statement really helps.
Unfortunately as an academic it's really hard to justify spending time on writing up a paper that's only going to be published on arXiv. Regardless of citations, it doesn't count towards your publication quotas or PhD requirements.
Science: "We do allow citations to papers posted at arXiv or bioRxiv." https://www.sciencemag.org/authors/instructions-preparing-in...
Nature (and other NPG journals): "Preprints may be cited in the reference list of articles under consideration at Nature Research journals...." https://www.nature.com/nature-research/editorial-policies/pr...
Cell (and other Cell Press journals, including Neuron and Current Biology): "Posted preprints may also be included in the References list with appropriate identification information...." https://www.cell.com/cell/authors
PNAS: "Preprints are cited as follows with a DOI or preprint ID number, and the date of posting...."https://www.cell.com/cell/authors
There is a trend to get pharmaceuticals to a stage where flow chemistry can be used (like a small version of the full blown basic chemicals processes). This is however still a research field because a lot of processes don't lend themselves to continue flow.
The most automation in chemistry can be found in the analytical side of things. A good example right now are the covid tests that are run on large automated liquid handling systems.
You know the whole deal with the three-body problem? How closed-form solutions become intractable in a hurry once you go past two or three mutually-influential orbiting bodies? As it was explained to me, that's why there's no SPICE for chemistry. Modeling exactly what happens when complex orbitals with dozens or hundreds of electrons interact with each other is one of those things we just have no clue how to implement in a practical application.
In principle it comes down to the fact that problem space explodes tensorially with number of electrons e.g. in principle you need grid size of 3N dimensions per every electron so for helium a^6 where a is number of points in the grid (and you probably need at least thousands of tens of thousands to accurately solve differential equations). This can’t be done in practice so other methods (expansion of problem in basis usually) are used.
Unfortunately solving Schrödinger precisely requires incredible amounts of both computing power and theoretical expertise. Qualitative results are up there for most systems thanks to developments in theoretical chemistry but it is still quite manual process that requires expert computational scientist to make sure results are reliable.
Pretty much all chemistry is some sequence of the above.
That is a very good question. In my PhD I built, among other things, a very primitive parallel stirring system in order to speed up the synthesis of test batches. Although a very crude device, there was tons of stuff that I needed to optimize.
There are many steps in the practical work that is chemical synthesis. It might be removing air, adding reagents in different elemental states, cooling/heating/keeping and certain temperature, observing the reaction, taking aliquots, terminating reaction, and the many steps of purification.
I'd argue that while automation is possible to a certain degree (continuous reactor systems are the most the most interesting IMHO), the resulting mashines are always problem (reagent, reaction, etc) specific. And here lies the problem.
The yields are quite bad though AFAICT.
I am chemical engineer and in my studies there was no programming and algorithm training at all (we did tiny bit of Scilab to solve some systems of equations but that’s all!).
Chemists in general (beyond theoretical and some open-minded exeptions) don’t program and don’t want to program. Synthetic/organic chemist still perceive synthesis as form of “art” ;) Therefore it would require huge shift in the mentality.
It is going to happen but not easily and and later than it could for social reasons :(
Besides, cleaning everything is very machine unfriendly.
I think there's theoretical value in this, and many have tried, but the incentives/disincentives for doing so isn't favorable. Here's some reasons why I think it's difficult to motivate people to publish negative results:
1) Negative results, while important in advancing science, don't get you grants.
2) Negative results need to be peer-reviewed -- there are "good" negative results (good protocol, failed result) and "bad" negative results due to bad data collection, wrong conclusions (bad protocol, failed result).
3) Given that it's so much easier to get a negative result than a positive one (as in anything there are only few ways to be right, tons of ways to be wrong), the volume of papers to review is orders of magnitude higher. Reviewers have to really sift to find the needle in the haystack. Between teaching classes, sitting on mindless committees, writing grants, mentoring grad students, etc. academics don't have that kind of time.
4) It would incentivize poor/mediocre labs to publish a lot of negative results to get their pub count up (these are the ones that currently publish unsubstantiated positive results in fly-by-night journals).
5) Bad faith authors may publish fake negative results to throw others off a promising line of inquiry.
(Note: some of these disincentives also apply to positive results in journals today)
The current method I know for exchanging "good" negative results is word-of-mouth, usually during post-conference drinks at the bar. (works for tech too!) I'm not sure if it's possible to arrange incentives in such a way as to make publishing "good" negative results worthwhile.
EDIT: there are exceptions. If the space of solutions is known a priori and bounded (say only n ways to do something), then publishing n results if even all n are failures is worthwhile. This situation doesn't come up all the time (the solution space is often open), but when it does, it's worth publishing all n results.
Then these could be classified by methodology/process/chemicals/etc for people to look up before starting their research.
In many fields, you can generally tell if someone is faking it about from how they respond and what they say. It's not that easy to fake specialized knowledge. This will help increase the signal-to-noise ratio.
The trick is to bootstrap a high-quality community, where top people in the field will want to engage. MathOverflow managed to do this for pure math. Quora on the other hand used to be good in the early days (many SV names) but has struggled to maintain quality. There's also the problem of moderating professional rivalry and reputation-maintenance in academia -- these are non-trivial issues in some smaller fields.
This reminds me of the problems to scale up EUV lithography which are bottlenecked on producing strong enough EUV light. They put in 20 kW of power to get out 200 W at the target wavelength of 13.5 nm, so light generation itself only has 1% efficiency, and then you need to reflect it at mirrors etc. to focus it (lenses don't work at those wavelength) and that makes only 2% of the light actually reach the waver [2].
[1]: https://www.laserfocusworld.com/blogs/article/16569161/the-s...
[2]: https://en.wikipedia.org/wiki/Extreme_ultraviolet_lithograph...
https://blogs.sciencemag.org/pipeline/archives/2010/02/23/th...
It seems there are multiple chemist-author hybrids out there!
Gentle and Forgiving nature...
(https://tribunist.com/technology/sr-71-blackbird-pilot-troll...)
“i always recommend a good pair of running shoes”, indeed.
It's interesting that chemistry for pharmaceutical purposes can involve similarly nasty substances.
The article also does a great job conveying how much of a frustration minuscule yields must be.
It is one of the oldest and very established fields. Unfortunately practices aren’t great. The preparation formulas are often vogue, imprecise and difficult to reproduce. This comes from the fact that often the sizes and types of glassware are not specified, some informations are omitted (how quickly something is changed not only to what value e.g. heat up to 100 degrees but it does not say over what time) etc. Chemists usually (except some theoretical/computational specialisations) don’t have any training in algorithms or programming.
There are novel developments such as https://www.gla.ac.uk/news/archiveofnews/2018/november/headl... and references therein. I’m optimistic about them but I expect strong opposition from older faculty. They see synthesis as more than art and think that one has to have “good hand” in order to be a good organic chemist.
I think some generational shift will be necessary in order to change this discipline to more reproducible, strict and reliable. It will come but not that soon :)
0.25 x 0.58 x 0.74 x 0.21 x 0.23 = .005 (0.05%)
I noticed only because something ticked on me when I read "0.005 (0.05%)", it "felt wrong" so I double checked.
I like HN in general, but this particular article gave me the same feeling that I had when I first discovered HN a couple of years ago.
Sticking to simple stuff; mines produce iron at a rate measured in thousand-tonnes per hour with yields of potentially sub-30% compared to volume of earth moved. Ammonia and many acids are presumably measured in tonnes or kilograms produced per day. Low yields make the process-oriented sad, but what matters is absolute ability to produce; not yield.
All that doesn't take anything away from this article; it just makes it hard to interpret what 'royal pain to synthesize' means in practice. The process isn't basic chemistry; but that isn't really saying much.
That would be true if reagents, labour and plant equipment were free, but unfortunately they are not. Consequently you have these strange creatures called process chemists who shave steps of the discovery synthesis, increase the yield, and get around difficult reactions. It's really quite magical.
When it comes to bulk chemicals like vinyl acetate (produced at scales of kiloton per day) another consideration is waste. It cuts into profit twice: you lose product and you pay for disposal.
If the inputs are expensive or the process can’t scale (to a billion doses in 12 months, say) then that’s worrying and perhaps indicates this isn’t a good candidate vaccine.
Something truly educational, and of course one of the people I wanted to educate was myself by forcing myself to learn much more than my high-school level of chemistry.
I've gone down this road a few times, each time giving up on the absolutely scale of even the most rudimentary understanding.
This really cracked me up. In my IT world the equivalent for "mutant" would be refactoring, right? I did "mutated" this way a few times in the past to much of the horrors of my boss(es)/manager(s) when they learned the next day.
If Remdesivir data looks good this month, there will be a rush to produce it, and if there’s only one published way to do that, then the ingredients for that one approach will potentially be hard to find. Thus we can benefit from different approaches which start from different raw materials.
Lots of cool Arxiv papers on this and Graph Neural Nets, Soft actor-critic, or Transformers can be interesting approaches. The transport theory seems like a good way to make a value function. How much time and money does it take to produce a given chemical by a given set of reactions? That’s a gajillion dollar question.
I spent way too much time last year looking at permutation-invariant distance metrics similar to Fused Gromov Wasserstein to invent an Atom Mover Distance, please let me know if you figure that out! DeepChem library is a solid framework, as are Tensorflow and Pytorch...
If anyone’s looking for a way to contribute to the COVID-19 response, open source data/algorithms to design synthesis pathways can be a strong approach. Everyone loves to use Deep Learning to design drugs, but it is valuable to design ways to make drugs, too!
Missed a trick not titling it:
“(1OO)OMG we made one gram...” :)
Scaleup from lab to pilot plant to production is another different beast.
I know that binding affinity has been shown not to be the best indicator of efficacy always, but I want to know if it's feasible, if someone can help
Apologies for the assumptions in these question, but are there many reactions in organic chemistry that are completely unknown?
This actually seems pretty fun. I'd love to have a reason to study it and a means to do something with my studies.
Well there are things we've experimentally tested, and things we haven't. Most reactions fall into the latter, and we can only make educated guesses about them.
If we could analytically solve the Schrödinger equation (we can't), we could accurately predict outcomes under perfect reaction conditions.
> are there many reactions in organic chemistry that are completely unknown?
Yes, the vast majority. But bear in mind that we can make educated guesses based on patterns, so we're not completely clueless.
There are rules, yes, of cause. But always the question: how much will react? And not: will I get the (reaction) product? But: how many (different) reaction products will I get. This is actually the reason why you need the purification steps after each reaction. Otherwise you would get an "reaction tree", in the end, byproducts reacting with other byproducts and you would get a, basically infinite amount of different end products.
Chemical formulae are required study for 11-14 year olds in England, for what it's worth. (Probably starting with words: methane + oxygen → carbon dioxide, and later writing a balanced equation: CH₄ + 2O₂ → CO₂ + 2H₂O.) The reaction presented is obviously much, much more complicated, but the same concept.
Edit: catalysts are covered too, although I don't know if the notation of putting them above the → is introduced at this age. https://www.bbc.co.uk/bitesize/guides/zqd2mp3/revision/6
Is there any evidence of this? It was a highly anticipated drug, but the first studies it was in showed only slight improvement over expected outcomes, far less than was seen with the chloroquine/zinc/antibiotic combo treatment.
I mean I think it's still interesting as a possible drug to add to the cocktail for maximum effectiveness. But let's not oversell it.
I did not understand this. liquid chromatography scales up quite well. There are other methods like Electorphoresis, salt precipitations etc., that don't.
Not compared to recrystallization or distillation. Try running a column on 10kg scale and get back to me.
(I worked in an o-chem lab for 2 years in undergrad, and the biggest we could do was ~20 grams at once)
It looks like the author is overselling some of the dangers here.
While you really don't want to dump n-BuLi into water, you have no reason to either.
The problem child of the class to which n-BuLi belongs is t-BuLi. That will spontaneously ignite in air, whereas n-BuLi will not. There was a very high-profile case I believe at UCLA a few years back in which a student using t-BuLi in the lab caused a fire with it and ended up dying.
https://cen.acs.org/articles/87/i31/Learning-UCLA.html
Also, I find this article confusing in the way it's written. Take the title, for example. It gives the impression that the author is describing his own efforts to make remdesivir ("we").
What he's really describing is some preps he found in the literature. And with a little too much hyperbole for my taste.
You're in luck, the first dose is 200mg, and then 100 mg daily after that.
Unfortunately, it's also IV, so you have a number of extra steps after synthesis to ensure sterility.
Good things to have on hand during this covid-19 pandemic (because you can't rely on hospitals to give it to you) are:
1) hydroxychloroquine sulfate taken orally 400 mg per day for a week ($180/kg on alibaba)
2) azithromycin taken orally 500 mg per day for a week ($150/kg on alibaba)
3) Camostat mesilate taking orally 200 mg three times a day for a week [1] ($50/g on alibaba)
4) favipiravir one dose of 1600mg two times on the first day, and then 600mg twice per day after that for a week [2]. ($40/g on alibaba)
5) covid-19 rapid test kits that use blood antibody tests and produce results between 3 to 10 minutes and cost about $1.50 per kit [3] .... although it looks like in the last week or so Alibaba has been blocking searches for these kits for some reason... although the search below does work but you have to look at the suppliers to find the ones that are actually selling it.
all this stuff can be bought on Alibaba and delivered in a week
[1] https://clinicaltrials.gov/ct2/show/NCT04321096
[2] https://www.medicalnewstoday.com/articles/anti-flu-drug-effe...
[3] https://m.alibaba.com/products/covid-19_rapid_test_kit.html
Stop suggesting people buy dangerous drugs from Alibaba. This should be bannable.
STEM = science technology engineering math
CS/software has huge demand and salaries to go with it (yay, ads)
Traditional (civil/mech/ee/chem/etc) engineering is OK. You'll never be rich but you'll always be comfortable. (I've had plumbers claim theye make more than me).
Science and math have really bleak career outlooks. Tenure track faculty jobs are a crapshoot. Industry jobs are scarce. Most people I know from college that went into the sciences are either still stuck in PhD or postdocs, or quit after BS/MS and now working as lab technicians or equivalent. Not even glorified, just plain old overqualified lab techs in QC departments and such.
Edit: As a final note, here's a 2015 report from OSPE, an organization representing engineering professionals in Ontario, Canada: https://www.ospe.on.ca/public/documents/advocacy/2015-crisis... An except:
"Information referred to in this report is derived from the Canadian National Census 2011 National Household Survey (NHS). According to the 2011 NHS1, only about 30 per cent of employed individuals in Ontario who held a bachelor’s degree or higher in engineering were working as engineers or engineering managers. Fully two-thirds of engineering-degree holders were not working in engineering at all. Many had jobs that didn’t necessarily require a university degree."
Plumbing is an essential service, and in emergencies, hourly rates can go into $300+/hour. So why don't everyone go into plumbing? Cause it's a shit job (/s)
Jokes aside, my take on the science job contradiction is that current American society does not respect nor want the products of science.
Think vaccines - when we have an emergency, everyone wants one, but in "normal times", think about where the pockets of the anti-vaccination movement have taken hold: rich communities in California where science should have had the best chance of surviving, but is struggling.
Communities in the American Southern states who are having trouble keeping evolution and science curriculums in tact are another example.
The solutions to the "STEM shortage" are usually to ramp up training (BS/MS/PhD students, occasionally postdocs), without actually creating viable jobs for these folks when they finish. Many smart people don't want to get involved in a cutthroat job market, so either leave the field or never enter it. (The people who "left" science from my grad school cohort were all very smart; almost all of them could have cut it)
Meanwhile, cutting-edge research is mostly done by trainees who are learning as they go. This is important--we need future scientists as well as current ones--but it limits the projects and pace of research.
This is totally fixable too--fund more staff scientist positions and dial down the number of trainees. The NIH funds thousands of studentships and just a few (~50?) "research specialists."
As for why people work at those salaries, there's more to life than money. Discovering stuff is fun and working on stuff that really helps people (not in the "expensive subscription juice service" sort of way) is rewarding. People teach for the same reason.
OTOH, taking a job at these wages is a luxury and chases good people out of the field. My spouse and I are both academic research scientists, but anything happens to one of us, the other's out ASAP.