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AlphaFold Found Possible Psychedelics
- dekhn 3y agoThey found a bunch of potential molecules that bind to 5HT2A, that's cool. But the really important conclusion of the paper, which I truly hope bears out, is that AF2 models are useful for finding binders to GPCRs. GPCRs are one of the largest (if not largest) drug targets and work (including my own) showed that modelling protein dynamics using expensive simulations was likely to be necessary to make accurate predictions of GPCR binding molecules. If this paper is correct, AF2 is actually able to recapitulate much of the detail that we previously believed required expensive dynamics simulations, which is about the best possible news because it suggests that we can omit these costly calculations. It's almost certain now that AF2 will win the Nobel Prize, as this represents some of the most exciting breakthroughs in the past few decades.
- keenmaster 3y agoAwesome. What are a couple examples of what this may enable (drugs that would target the same)?
- deleted 3y ago[deleted]
- oh_sigh 3y agoA better question would be what don't they enable. Something like 30+% of FDA approved drugs operate along that pathway. Besides for the boring stuff like being able to trigger cancer cells to kill themselves, we could have a molecule you inject into your eye that opens up your vision to be able to see infrared and ultraviolet light(and beyond).
- dekhn 3y agoI would extrapolate from existing GPCR-targeting drugs, but the list is so long I can't really give a survey. As a protein family, GPCRs do a lot of different things but basically it boils down to signal transduction: there's something outside a cell and the cell wants to know that so it can respond. GPCRs mediate the binding of the thing outside to the surface of the cell, and transduct the information into the cell, delivering a signal to the nucleus, where the transcriptional machinery is manipulated to make new proteins (or stop making proteins). I left out some steps. This allows the cell to be responsive to the outside in a secure way, without the outside molecule coming into the cell. The 3d structure of GPCRs is intimately associated with the cell membrane, with one end sticking out into the extracellular environment, and another sticking out into the interior of the cell. Simply getting that sort of protein, which is not soluble in water, was a massive challenge and the first structures didn't arrive until the early 2000s, when people learned how to crystallize GPCRs using detergents and other solvents that simulated cell membranes. So many diseases are caused by misregulation of signal trandsduction... let's take some examples. Vasopressin is a drug that also happens to be a natural body product. In some diseases, people don't make enough vasopressin to regular the kidneys properly, leading to a form of diabetes, and simply giving people more of it helps reduce the symptoms. I guess in this case (not certain), people have mutations in the GPCR that receives the vasopressin signal that attenuates the signal "too much" and by just dosing the patient with a lot more, more signal gets sent inside the cell. I think at least 10 nobel prizes in medicine have been awarded for research related to GPCRs, which has uncovered a wide range of medically relevant knowledge of physiology. The NERSC supercomputer "Cori" was named for Gertie Cori, who helped discover some of the core mechanisms in human metabolism during the golden age of metabolic biochemistry. I think the simplest way to think about it is being able to target specific GPCRs is like being in front of a switchboard with buttons and knobs controlling every critical detail of how human bodies manage and maintain themselves.
- chpatrick 3y agoEvery time I hear about some complex function of the cell it really makes me amazed at us being collections of trillions of them...
- Duanemclemore 3y agoEven if it has to go through extensive vetting on the theory side, the ability of these models to improve the starting points for the harder process is very strong. For example, in daylight modeling - and this is 2-3 years ago mind you - it has been found that "AI" is perfect for getting 99% closer to the actual luminance in something like 01% the computation. So that even if you feed those "precalculated" models into the traditional raytracers, the improvement to the first guess is well, well worth it.
- dekhn 3y agoI don't really think there's an analogy between radiosity simulations and drug discovery. Drug discovery will probably remain fairly ad-hoc and stochastic for some time.
- darig 3y ago[dead]
- Duanemclemore 3y agoThat's fair. I was merely talking about being able to make better "first guesses." Nowhere near the predictability of a much simpler phenomenon such as radiance mapping, of course. And as the article says, >"There were a lot of models that we didn’t even try because we thought they were so bad,...”
- vlovich123 3y agoTo me the analogy is that generic AI techniques with minimal problem domain specific adaptations is providing huge boosts in efficiency on a wide variety of applications. It’s like the steam engine causing mechanical work to become vastly more efficient. AI in many ways is doing the same thing to intellectual work.
- Radim 3y agoThe long-anticipated "mental gym" industry! Where people pay to exercise their atrophied mental muscles the same way they exercise their barely-needed physical muscles now. With all the associated bullshit and snake oil and romanticizing of the "good old natty days" too, of course. Big $$$ industry, at least until our brave future does away with voluntary reproduction altogether, being the reason people bother to exercise in the first place.
- mtlmtlmtlmtl 3y agoAs an expert, do you think this might lead to easier discovery of much more selective GPCR ligands? Agree finding new 5HT2A ligands is cool, what would be truly interesting is having a suite of totally selective ligands for the entire 5-HT receptor family. I feel like a lot psychopharmacological research into a lot of mental disorder is severely hampered by 1) the complex interactions of the various serotonin receptors(and also with other receptors like D2) 2) the lack of truly selective ligands, necessitating a hodge podge of less selective agonists combined with selective antagonists to isolate the effects of specific receptor interactions(making it hard to do this sort of thing in humans). Or using knockout mice, which is also kind of dubious because you've now made a fundamentally different brain and are trying to compare it to a normal one. And that's sort of hit and miss.
- timr 3y ago> But the really important conclusion of the paper, which I truly hope bears out, is that AF2 models are useful for finding binders to GPCRs. What seems more likely to you: that the (obviously worse [1]) structures produced by alphafold are somehow magically capturing protein dynamics, or…the virtual screening method (DOCK) used by the paper is relatively insensitive to the quality of the underlying model? They say right there in the article that neither screen was obviously enriched versus the other (the hit rate was around 50% for both branches, which was essentially the same as for the very different retrospective screening method in the same paper [2]). I’m putting my money on the parsimonious answer. Running structures through a large enough virtual screen will almost always find something that binds, even if the starting model isn't great. [1] at least in the case of the x-ray structure. Using a cryoEM structure is sort of interesting, since they’re blurry anyway. [2] the actual paper: https://www.biorxiv.org/content/10.1101/2023.12.20.572662v1.full.pdf https://www.biorxiv.org/content/10.1101/2023.12.20.572662v1....
- dekhn 3y agoI don't have a good answer. I don't think AF2 captures protein dynamics in a direct way. And I think docking programs have inadequate loss functions and sampling to really find accurate answers. I suppose AF2 could be modified to produce structures that are well mapped to docking programs. I think you're right, that running lots of structures through virtual screens will find lots of binders. In the middle days of protein design (well before AF2), people went from not being able to design anything, to being able to easily design "rocks"- proteins that were exceptionally stable, but didn't do anything interesting. Their loss function that they maximized for was stability. But that comes at a cost: high stability seems to prevent lability (which is useful for enzymes). And I think that if you just pass enough stuff through a good-enough program, you'll find lots of binders, but those binders won't actually be useful binders. We already know from decades of work that we can find lots of nanomolar and picomolar binders that don't do anything worthwhile (or have side effects). My conclusion is: we may very well be on the tip of solving another bottleneck in the drug discovery pipeline, or, we might just be bumbling around in a big space and found a nice local minimum. I don't think AF2 or similar systems on their own are going to completely solve the problem, and until DeepMind or somebody else creates something that can truly address the hard problems in human biology, the most we can hope for is finding more effective new drugs more cheaply and somehow couple that with improvements in genetic personalization.
- ChatGTP 3y agoIt's almost certain now that AF2 will win the Nobel Prize, as this represents some of the most exciting breakthroughs in the past few decades. It seems weird to give the peace prize to a model. What about the people who made the model, or is that what you mean?
- ignoramous 3y ago> It's almost certain now that AF2 will win the Nobel Prize ... most exciting breakthrough Unsure of the merits of this finding myself, but I'd be cautious given just this past week DeepMind was accused of publishing gibberish by Chemists: https://twitter.com/Robert_Palgrave/status/1744383965270581615 https://twitter.com/Robert_Palgrave/status/17443839652705816...
- dekhn 3y agoThat is unrelated work by a different team. The merits of AF2 have been demonstrated already and accepted by the community (which has reproduced those results both theoretically and experimentally) although the community is still arguing about just how good AF2 (and its descendants) are. I agree the chemistry paper does not look g ood but it's hard to say yet. Like I've said elsewhere DM and Google Researchers are under huge pressure to make their results look as good as possible. Already people have serious concerns about another Google paper using ML for IC layout.
- deleted 3y ago[deleted]
- candiddevmike 3y agoThese will inevitably become "research chemicals" and potentially ruin a few psychonauts eager to try them.
- morkalork 3y agoThe stories of people getting limbs amputated because the RC they were shipped by accident was an insanely strong vasoconstrictor while still tripping 24h after dosing is the stuff of nightmares.
- aaroninsf 3y agoTrue, and also, the stories in Phikal and Tikal of more disciplined people following titration protocols etc. and discovering hidden kingdoms of the mind, is the stuff of dreams.
- fb03 3y agoThat's curious. Where can one read such stories? eRowid?
- zoklet-enjoyer 3y agoProbably. Or bluelight. Bromo dragonfly back in like 2005ish
- notamy 3y agoiirc the specific incident being referenced is Bromo-DragonFLY being mislabeled as 2C-B-FLY.
- zoklet-enjoyer 3y agoMislabeled and tainted RCs became enough of a risk when fentanyl and fentanyl analogs started gaining popularity that the guys I knew who were ordering from overseas just stopped messing with RCs. It's disappointing that it ended up like that. 2009-12 I had access to pure and dirt cheap 2C-E, methylone, 4-ACO-DMT, the JWHs, ketamine, methoxetamine, LSD (I don't think there were any analogs available yet?). It was a crazy time.
- man4 3y ago[dead]
- zbowling 3y agoAI takes our jobs, and then it helps us get high. I love this future.
- yazzku 3y agoYou're gonna be tripping like that cat astronaut on the moon, alien spaceship background, synthwave style.
- deleted 3y ago[deleted]
- lgkk 3y agoPrecursor to the matrix.
- WitCanStain 3y agoIf only you'll have the money to get high after you no longer have a job.
- ChatGTP 3y agoHaven't you heard from SV types, there will be an abundance of everything if AGI is invented.
- simple10 3y agoSome have speculated psychedelics like psilocybin played a pivotal role in Homo Sapiens consciousness development. If true, the poetic symmetry of AI inventing psychedelics and smart drugs to fuel the next jump in human development is fascinating. Is there a genre of scifi for this yet?
- tedivm 3y agoWhat you're referring to is called, no joke, the Stoned Ape Theory. It is not really taken seriously at the moment, as it completely lacks in evidence. https://en.wikipedia.org/wiki/Stoned_ape_theory https://en.wikipedia.org/wiki/Stoned_ape_theory
- gamepsys 3y agoHowever listening to it's originator Terence McKenna explain the stoned ape theory while intoxicated on the correct substances is a very fun experience.
- aurareturn 3y agoThe author must have wrote that book when he was high on shrooms.
- kjkjadksj 3y agoAn animal in the wild will try to eat everything and learn through trial and error what is good to eat and what isn’t. Selection might even lead to behavioral changes that naturally predispose one to one substance over another. I don’t see why humans would be any different.
- gitaarik 3y agoTrial and error? I think most animals have an instinct of what they should eat when. I think humans have that too, but kind of forgot about it, or at least we are out of touch with these things and mostly judge intellectually.
- 3y ago
- uptownfunk 3y agoWhy not just take with chocolate like everyone else
- ProllyInfamous 3y agoGnutella-dipped caps, with peanut-butter stalks.
- uptownfunk 3y agoWow love it
- m3kw9 3y agoHas any discoveries or drugs ever reversed autoimmune conditions? This look like one of the big end level bosses for AI
- freedomben 3y agoAs someone who suffers greatly from an autoimmune condition, I could not agree more. It makes me wish I had developed a background that would allow me to work on this problem. Once my kids are raised, if it hasn't been done already at that point, I'll be giving it a try
- WhackyIdeas 3y agoIt’s a brave new world.
- mondayp 3y agoAlphaFold’s potential in revolutionizing drug discovery, especially in the realm of psychedelics for antidepressants, marks a significant paradigm shift.
- PessimalDecimal 3y agoAs a total outsider to this area of research, why is AlphaFold's potential most notable specifically for psychedelics?
- cowsandmilk 3y agoThe protein class they bind to has historically been hard to determine structures experimentally and had high flexibility. The preprint that is the basis of the article showed that using traditional virtual screening against Alphafold structures had similar success rates as screening against experimental structures. Additionally, the Alphafold structures may represent alternative conformations of the proteins not seen in the experiment-derived structures.
- uticus 3y agoThese tools are unlocking all sorts of things. For a darker side of the toolbox, ref https://news.ycombinator.com/item?id=36906214 https://news.ycombinator.com/item?id=36906214
- juggertao 3y agoAre there any protein based psychedelics? I thought they are all small molecules.
- Synaesthesia 3y agoNot to my knowledge.
- iancmceachern 3y agoI find possible psychedelics on the sidewalk in my neighborhood in San Francisco quite regularly, coincidentally. /s
- refurb 3y agoHere is maybe a helpful analogy for the challenges behind protein folding. The 5HT2A receptor is comprised of 471 amino acids. Proteins fold based on interaction between each of those amino acids, plus the external environment (plus post-translational modification). So imagine a length of string with 471 magnets attached to it with different magnetic field strength (ranging from weak to strong). Now imagine it floating in space - how would the string fold upon itself? Now imagine different segments of the string have different stiffness - some are super flexible, others are rigid. Difficult to predict how it might fold? Oh yeah! Now imagine the string not in space, but in a solution of water with a number of other magnets floating in it (the cytoplasm within the cell and external environment outside the cell). Oh, and also imbedded in a wall with magnets in it as well (the cell membrane). Now how does it fold? Ok, now we think we have a good approximation of the structure just based off of various pieces of data. But wait! Other proteins are also embedded in the cell wall - all with their own structures and selection of weak and strong magnets, stiff and flexible segments interact with the receptor! Oh, and not all receptors have the same external environment - that can differ based on another laundry list of factors. Yeah, that's another layer of complexity. Then add on top that the external environment (inside the cell on one side, outside the cell on the other) has a ton of water, but also dissolved ions like Na+, K+, Cl-, plus a bunch of other proteins with their own "magnets" and "stiff and flexible segments". These outside molecules will bind and disassociate from the receptor on a constant basis, ever so tweaking the shape. Oh crap. Of course AI has a good starting point - we can get x-ray crystal structures of the receptor and identify the position of atoms. But wait! That's in a crystal, not a human body! So that structure is likely incorrect in several ways. We also have other analytical techniques that can give clues to the structure in a solution that more closely mimics what's inside the body, so we can at least tell when the x-ray structure is just wrong (at least in some places). That should give people an idea just how complex protein folding is. AI is good at taking all known "rules" about protein folding (and molecule interactions with the protein) to improve the predictions. But we're still far off from truly understanding all the factors in play.
- var_cw 3y agoAI's matching hit rates in drug discovery is a game-changer. It's like AlphaFold just gave us the GPS coordinates for the biochemical treasure we've been blindly digging for. LSD's serotonin link and the quest for similar, safer compounds?
- jampekka 3y agoLSD is a pretty darn safe compound. And there are a lot of similarly safe compounds with "smoother" psychological effects. The War on Drugs is a lot bigger hinderance than technology here. And the patent-driven pharmabusiness too.
- deleted 3y ago[deleted]
- freedomben 3y agoExactly. If they discovered any new drug that has even a tiny potential for recreational use, it's going to be banned quickly. Hell, we can't even have certain plants that grow entirely in nature. No way they're going to allow new synthetics. Unless our current social Puritan outlook starts to change, it's not gonna happen. And we're headed toward puritanism right now, rather than away from it, so I wouldn't be counting on it during our life times
- ogogmad 3y ago> LSD is a pretty darn safe compound It can cause permanent damage to vision or sanity. Sure, it's not addictive, and you practically can't overdose -- but it makes you temporarily insane and blind, and both of these can persist without there being a cure. And just, a general point: [CITATION NEEDED]. Unlike you, I'll provide my own citation: https://www.frontiersin.org/articles/10.3389/fpsyt.2017.00240/full https://www.frontiersin.org/articles/10.3389/fpsyt.2017.0024... > And there are a lot of similarly safe compounds with "smoother" psychological effects. If you mean the party drug 2C-B, it has some similar risks.
- jampekka 3y ago
- boxed 3y agoAfter seeing all the fakery from how AI models handle programming (being "very good" at tasks where it turns out it was just trained on the problem in the first place), I do wonder if AlphaFold is likewise a red herring. How many of the AlphaFold protein folding predictions have been actually verified by a lab? And are any of those actually new proteins or just tiny variations on things in the training set?
- hulium 3y agoThe linked article is not about AlphaFold itself, but about researchers verifying AlphaFold's predictions. So the article directly addresses your question. Unfortunately HN only shows the first half of the headline. > Shoichet agrees that AlphaFold predictions are not universally useful. “There were a lot of models that we didn’t even try because we thought they were so bad,” he says. But he estimates that in about one-third of cases, an AlphaFold structure could jump-start a project. “Compared to actually going out and getting a new structure, you could advance the project by a couple of years and that’s huge,” he says.
- mechagodzilla 3y agoAlphafold was largely trained on the structures in the Protein Data Bank, which includes lots of important protein targets. Just by virtue of evolutionary biology, many proteins are variants of one another, so if you're doing something like running AF on all of the proteins in the human body (only 24k or so), you're including lots of targets that were part of the training set, lots of targets that are closely related, and lots of targets we don't have structures for. Many (most?) proteins are also dynamic, so can't really be represented well by a single structure (rotating a sidechain inside a pocket when using a structure for virtual screening will frequently yield completely different 'hit' molecules, for instance). AF2 is a better guesser than previous tools for a reasonable structure, but that's like 1 step in a 47 step process to 'finding a new drug.' Even here, "AlphaFold" didn't find the drugs candidates, it generated the structure(s) that they ran common virtual screening tools on (which introduces all kinds of other assumptions and approximations depending on the tool).
- Kiro 3y agoI don't agree with your assessment of AI and programming at all. In fact, I think you're being insidious when framing your skepticism against this using words such as "likewise", making it sound like it has been unanimously agreed upon. It's like me saying "after seeing that the earth is flat, why wouldn't other planets be flat?". AI is definitely capable of solving novel programming problems and I'm constantly surprised how well Copilot understands the esoteric context of my code.
- msla 3y agoIs it time for a new take on PiHKAL? https://en.wikipedia.org/wiki/PiHKAL https://en.wikipedia.org/wiki/PiHKAL > PiHKAL: A Chemical Love Story is a book by Dr. Alexander Shulgin and Ann Shulgin, published in 1991. The subject of the work is psychoactive phenethylamine chemical derivatives, notably those that act as psychedelics and/or empathogen-entactogens. The main title, PiHKAL, is an acronym that stands for "Phenethylamines I Have Known and Loved." > The book is arranged into two parts, the first part being a fictionalized autobiography of the couple and the second part describing 179 different psychedelic compounds (most of which Shulgin discovered himself), including detailed synthesis instructions, bioassays, dosages, and other commentary. https://en.wikipedia.org/wiki/TiHKAL https://en.wikipedia.org/wiki/TiHKAL
- Mizza 3y agoI've made a DeepShulgin but need somebody with some spare GPU cycles to do the 'big crunch'..
- deleted 3y ago[deleted]