11 ms·

AlphaFold won’t revolutionise drug discovery

203 points by panabee 4y ago

bilsbie 4y ago
> Forming these coils, loops, and sheets is what proteins generally do, but ‘why?’ doesn’t enter into it. How do we know the model hasn’t figured out some of the ‘whys’ somewhere in there?
- bigdict 4y ago
  Because it learns a conditional distribution. It doesn't work on figuring out why the distribution is the way it is.
- lrem 4y ago
  Your question fundamentally falls into the area of unanswerable philosophy akin to "do insects feel pain?" But there's a reasonable intuition suggesting that the answer to your question is "no". What we're looking at is a non-linear regression model reproducing the function (which according to the article isn't really a function, but that's above both my and the model's knowledge) from a gene sequence to a 3d structure. It is heavily meta-optimised, so the "why's" would only be in the model, if reproducing the process of folding the protein was the cheapest way to guess the structure (). Intuitively it introduces at least one extra dimension, so should be way more expensive than finding analogues among known sub-aspects of the function. Hence, I would expect none of the "why's" to be in there. Sadly, if any insight for the "why's" was there after all, we don't have a method to extract it anyway. Disclaimer: I work in Google, far away from DeepMind, have no internal knowledge on this.
  - salty_biscuits 4y ago
    "Sadly, if any insight for the "why's" was there after all, we don't have a method to extract it anyway." This has been my central frustration with working in ML. People always expect a "why" to exist, and by why I mean a cogent narrative explanation to complex phenomena. Maybe there is no "why" like this for a bunch of physical phenomena, maybe it is just a bunch of low level intricate stuff interacting in complex ways. There might be an emergent model that you can get a useful predictive model for with an ML model, then people get mad because the prediction doesn't solve the real meta problem that they were expecting to solve via the sub problem (e.g. solve folding then get mad because folding itself turns out not to be super useful because we don't know which protein to target, solve image classification then get mad because that doesn't make it easy to make a self driving car, etc, etc). "More is different" is definitely an idea in physics that needs to propagate into other fields to temper our expectations.
- sgt101 4y ago
  The "why" is a bit of an odd question anyway - the structure is as as the structure is. It's like asking why "red hears a galaxy", just words.
  - freemint 4y ago
    Well, no. A bunch of mechanism have models of lower complexity that have almost exactly the same predictive power but a completely different structures. Those higher order structures are the "why". Why did does a cube on a inclined plane start to slide? You act like the correct answer is "because the subatomic particles and space time in the light cone of the experiment made it that way" when one should expect "because the sin of the incline angle times mass times local gravity became bigger then the static friction between a cos(incline angle) times the original cube weight and the surface at no incline" which is a lot simpler.
    - visarga 4y ago
      When the process being studied has more moving parts than humans can grasp in working memory there comes a limit after which we can't understand it anymore. Maybe the nice clean abstract concepts we expect are not there. It could be perfectly correct and yet complicated like front-end spaghetti code.
      - tsimionescu 4y ago
        That is possible, yes. But that doesn't make the question of "why?" meaningless, as the GGP was suggesting.
        visarga 4y ago
        I see neural nets as extensions of human thought - they explore the fuzzy depths where we can't grasp directly, like a microscope or a telescope enhance vision. They are enhanced correlation engines.
        4y ago
- pelorat 4y ago
  It's probably not even possible for a human to understand the "why"
aabhay 4y ago
The author doesn’t answer the question. If not this, then what will? Because as far as I can tell, we’re nowhere near extracting the full value of AI-generated protein structures. Why plant this flag and be wrong later if you have no real idea of what should be done instead?
- ChrisRackauckas 4y ago
  The questions he asks here are exactly the questions that quantitative systems pharmacology (QSP) seeks to answer (and as a result, it's booming as a field). Just because you can build a drug to inactivate said protein doesn't mean you should. 85% of clinical trials fail as he states, and one of the main reasons why is because the target ends up being incorrect. Targeting some protein because a lot of it seems to exist when a given disease is occurring might end up targeting the symptom instead of the cause. Understanding how the complex systems interact, their feedbacks and their nonlinearities, is essential to knowing what needs to be targeted. We had already been able to quickly create new drug candidates, and with protein folding predictions we can now do that even faster. Those drugs can be tested in a lab to see if they bind to the proteins they're supposed to, and they keep getting quicker at hitting exactly the function they expected. But without making the billion dollar clinical trial more likely to be solving the actual problem, we're still going to be limited by "okay, so what in this pool of possible drugs should we risk trying next"? We can accurately knock out protein function, but we're still fishing in the dark when it comes to how to actually fix and regulate bodies.
- echelon 4y ago
  Because we have to be honest with ourselves. Don't tell the crystallographers they're no longer necessary for structure determination. If people flee the field and ML doesn't pan out, then we're worse off. Treat this as it is. An exciting approach that may help some now and yield fantastic results in the future. Don't count the chickens before they hatch. Even if the structures were entirely correct - and they're definitely not - there's a massive complex metabolome to figure out. Google is certainly milking the PR as much as they can, and that can be dangerous to the laymen approving research budgets.
  - 4y ago
microSnowball 4y ago
I think alphafold gets hated on too much. It won’t revolutionize things but I bet people are out there right now looking at different structures and motifs only seen on alphafold to get a better idea on how existing drugs bind and affect them. And then designing analogues and so on. Time will tell, I guess. It’s kind of like anything in research, lots of small steps enable revolutionary breakthroughs every so often.
- fabian2k 4y ago
  You can assume that any known drug target has experimentally determined structures available, once you spend the enormous amounts of effort necessary to put a drug through real clinical trials the effort to determine the target structure is pretty much irrelevant. Of course there are plenty of drugs where we either don't know where they bind or we're probably wrong about where we think they bind. Or they bind at multiple places and some desirable or non-desirable effect are due to binding at places we don't know yet. There are real uses to having lots of high-quality structure predictions for proteins. Drug development is something that only get limited benefits here. If you want to know how drugs or drug candidates bind to proteins you first create a protein structure with X-ray crystallography. Then you soak your crystals with your drugs or drug candidates and determine even more structures. The interesting part here is not necessarily the overall fold of the protein (which is mostly what AlphaFold gives you) but e.g. a single hydrogen bond to the drug in the active pocket of the target protein. You need really high-quality data if you want to do any kind of rational drug design, most of the time we still just semi-randomly vary structures until they bind better as far as I understand.
- epistasis 4y ago
  I think it gets marketed too much and hated on too much. Given the utter dominance of Google advertising, I think the hating is a necessary counter in order to at least place it in its right place. Whatever skill Google has computationally is more than matched by their media dominance and public relations prowess.
  - mtlmtlmtlmtl 4y ago
    I find this view very strange. If you apply the same logic to politics, the outcome is pretty grim. And we've been seeing more and more of that. I don't like hype or hate that's devoid of nuance. But actual scientists working in these fields don't generally pay attention to these things as much as we might. They read the papers, and they have years of training to help them decide what is overhyped and what isn't. I'm not sure what happens on HN or in advertising channels has such a huge bearing on this.
lrem 4y ago
I love this article. It nicely answers the question I posed (https://news.ycombinator.com/threads?id=lrem#32263287 https://news.ycombinator.com/threads?id=lrem#32263287) in the discussion of the original announcement: is today's db good enough to be a breakthrough for something useful, e.g. pharma or agriculture? And the answer, somewhat unsurprisingly, seems to be "useful, but not life-changing". And that's a perfectly good result in my eyes :)
- frozencell 4y ago
  OpenAI's GPT-3 and DALL•E2 might be life-changing for their creative users, writers and illustrators or beginner creators, I can't remember any life-changing use case for groups (outside of the creators themselves). For ML researchers, transformers seem to not be used as AGI at all (despite general or multi-modal potential) but mostly used for test and probability tool.
  - p1esk 4y ago
    For ML researchers, transformers seem to not be used as AGI at all (despite general or multi-modal potential) but mostly used for test and probability tool. What do you mean?
    - frozencell 4y ago
      They are tools to help create the next tools.
  - abrax3141 4y ago
    Life-changing like you mean by destroying creatives’ way of making a living?
- SilasX 4y ago
  That link goes to the top of your comment history. I think you want this link to ensure you see the right comment: https://news.ycombinator.com/item?id=32263287 https://news.ycombinator.com/item?id=32263287
xiphias2 4y ago
While the article is correct that knowing the protein structures in itself is not that interesting, it's a prerequisite step to predicting interactions between proteins, which is super interesting for drug discovery. What's encouraging is the rate of progress, not what has already been done.
- ramraj07 4y ago
  It’s not necessarily a pre-requisite: many of the more “exciting” drug platforms like Antibodies (which comprise like 90% of approvals nowadays anyways) in principle DONT NEED any structure info a priori. You don’t need to know a proteins structure to generate antibodies, just the protein itself to either inject into an animal or pass through a phage display column. Now typically drug companies will of course only work with proteins whose structure is known. Please note that the top drug companies have some of the best crystallography departments and they will most definitely get the structures done experimentally (if it’s possible). AlphaFold will likely help with that for sure. But it’s just one of those things that’s just a nice to have. If a protein is truly important and you cannot tell it’s structure you can still go ahead and make an antibody drug against it and Even characterize it using mutagenesis analyses.
dekhn 4y ago
To revolutionize drug discovery, you need to solve a number of problems that ML can't really address right now. We do not have well-formed theories of the molecular details of many diseases. There is no immediate computational approach that address this defect. The community has had fairly simplified models for some time, and there's a lot of historical belief that by knowing protein structures in details, we can understand the nature of a disease through its molecular etiology, and from that, we can make drugs (either small molecules or biomolecules) that modulate proteins in rational ways to eliminate the disease with a minimum of side effects. In my mind, much of the problem is similar to modern deep learning compared to previous techniques. Several extremely challenging problems (high accuracy voice recognition, image recognition, object detection) simple were not solvable through the statistical techniques and mental models adopted by the practitioners. It is not abundantly obvious that stupidly simple deep networks can be pretrained on enormous amounts of labelled data, or even unlabelled data, but we didn't even have the ability to know this confidently until we had the right network architectures, enough high quality labelled data, and adequate compute power to train them. I believe that by starting to think about disease modelling from the same mindset as deep learning (simple models with many parameters, the models don't actually represent the assumed mechanism, large amounts of high quality data, lots of CPU, GPU, and RAM) and also thinking of the disease treatment process in the same way will greatly increase our ability to "understand" and "treat" diseases, while knowing far less about their underlying mechanism that we thought. A common example is disease/patient stratification. If you've developed drugs that treat disease A, but it turns out later, there are really two diseases, A1 and A2 with different underlying mechanisms but superficially similar exterior symptoms, you'll realize why some percentage of your population didn't get better (and often got worse, given the underlying toxicity of some medicines). If we could just stratify diseases better, and classify patients into the right bins, the effectiveness of medicine will go up (and drugs will get through clinical trials faster/better). None of this addresses the later-stage issues, such as successfully running all the phases of a clinical trial and the other gauntlets you must pass in order to get a drug FDA-approved. I would continue to expect marginal improvements for the foreseeable future. But be aware: some companies already have managed to do a good enough job developing new medicines that they routinely create multi-billion-dollar blockbuster drugs year after year after year (my employer, Genentech, is a perfect example of that). It maintains an enormous and well-funded R&D arm that expends untold neurons attempting to understanding disease better even before we start to consider something as "druggable".
curious_cat_163 4y ago
Mapping DNA sequences to 3D protein structure is the problem that the AlphaFold tries to solve. I don't think it tries to solve for "drug discovery". I suspect that, like any ML problem, this one is a small part of the whole solution of drug discovery. There are always system-level dynamics at play. To me, some relevant questions before deciding to take on an ML problem tend to be: [x] Does solving it eliminate manual labor from the process? [x] Does it save $ in the progress towards solving the whole problem? [x] Is it fun to solve it?
pcrh 4y ago
This article makes some good points, but is incorrect on some others. In particular the statement " It is very, very rare for knowledge of a protein’s structure to be any sort of rate-limiting step in a drug discovery project!" does not reflect the realities of drug discovery. Knowing a protein's structure, and its structure when complexed with ligands/drugs, is a massively important bit of data in the armoury of medicinal chemists, which Derek Lowe knows all too well. Of course, it may be that knowing which protein to target is more important, and that problem isn't affected by AlphaFold.
zmmmmm 4y ago
This take seems to be somewhat over sceptical and slightly over-reaching to me. > when your entire computational technique is built on finding analogies to known structures, what can you do when there’s no structure to compare to Lots of people seem focused on the idea that deep networks can't do anything novel and are just like fancy search engines that find a similar example and copy it. This is not true. They do learn from much deeper low level structures in the domain they are exposed to. They can be aware of implicit correlations and constraints that are totally outside what may be recognised in the scientific understanding. Hence AlphaFold is quite capable of predicting a structure for which there is no previous direct "analogy". As long as the protein has to follow the laws of physics then AlphaFold as at least a basis to work from in successfully predicting the structure. > It is very, very rare for knowledge of a protein’s structure to be any sort of rate-limiting step in a drug discovery project! This and the following text are very reductive. It's like saying, back in 1945 that nuclear weapons would not be any sort of advantage in WW2 because it is very rare for weapons of mass destruction to win a war. Well yes it was rare, because they didn't exist. And so too did we not have a meaningfully accurate way to predict protein structures until AlphaFold. We've barely even begun to exploit the possible new opportunities for how to use that. And people have barely scratched the surface in adapting AlphaFold to tackle the related challenges downstream from straight up structure prediction. Predicting formation of complexes and interactions is the obvious next step and it's exactly what people are doing. It's not to say that it will revolutionise drug development, but the author's argument here is that he is confident it will not and he really doesn't assert much evidence of that.
- ramraj07 4y ago
  If you’re gonna get mad and quote a sentence to rail on the author, at the least quote the full sentence: the author ends it with “and there never will be.” Because among other things he’s talking about intrinsically disordered proteins[1]. What can the best prediction model do to predict the truly unpredictable? Just tell us that it’s unpredictable. And what is your second criticism exactly? The author comes from the drug discovery industry. What the author said should be generalized to: even if we know the perfect experimentally confirmed 1A resolution structure of every protein out there tomorrow, that won’t exactly revolutionize drug discovery. That’s because protein structure gives maybe 10% of the context you need to successfully design a drug. It’s dynamics, higher order interaction specifics, complex interplay in signaling pathways in particular cells in particular contexts and what entire cells and organ systems in THAT PARTICULAR ORGANISM do when this protein is perturbed, are what truly affects drug discovery. If you absolutely want to revolutionize DD, find us a better model to test things on that’s closer to the human body as a whole. Currently mice and rats are used and they’re not cutting it anymore. This fundamentally goes back to the downfall of the prototypical math or software guy trying to come and say “im gonna cure cancer with MATH!” No you’re not. You’re gonna help, and it’s appreciated, but if you’re gonna truly cure cancer you better start stomping on a few thousand mice and maybe also get an MD. 1. https://www.nature.com/articles/nrm3920 https://www.nature.com/articles/nrm3920
summerlight 4y ago
The modern world is not that simple enough to allow a single paper or technology to revolutionize anything. I don't understand why people are reiterating this obvious fact over and over? Most of the technological breakthroughs are usually a culmination of decades of research and investments.
- evouga 4y ago
  My observation is that breathless hype pieces proclaiming that a new technology will imminently revolutionize area X outnumber the articles expressing common-sense skepticism about the technology, by two to three orders of magnitude.
  - yomkippur 4y ago
    lot of VCs have connection to media outlets, journals. its interesting that as VCs are imploding, we see more of the hype going away. somebody bet big on AlphaFold and OpenAI but realize they are not going to break even at least for another decade or two.
    - abrichr 4y ago
      > VCs are imploding, we see more of the hype going away. Do you have a source for either or both of these claims?
AlbertCory 4y ago
In the early 2000s, I took a bunch of UCSC Extension courses on mol bio, bioinformatics, and drug discovery. Back then, abundant DNA information was the thing revolutionizing the field. What the scientists (all from Roche) said was, more or less, "yeah, that helps a lot. It doesn't solve the whole problem, though." 20 years later they've gotten yet more help with Alphafold. Once again, they can do things faster, but it isn't a Moore's Law-type of change. It's still a really hard problem demanding culture, animal, and human tests, and those take time and money.
- fny 4y ago
  AlphaFold is in part impressive because a team with no protein design experience solved a challenge which had eluded experts for decades. It's absolutely a step change in demonstrating what's possible. The author can sit and criticize alpha fold from his armchair, but the dude is missing the forest for the trees. Do you remember how people on HN used to take comfort that AI couldn't write code? Now they're taking comfort in the fact that AI can't write code well. We're headed towards a word where data gets put in a blender to decide what to try next. That entire pipeline can and will be automated. And in that world, human ingenuity and expertise may become far, far less relevant.
  - coldtea 4y ago
    >We're headed towards a word where data gets put in a blender to decide what to try next. That entire pipeline can and will be automated. And in that world, human ingenuity and expertise may become far, far less relevant. Let's hope we'll be dead by then...
    - osigurdson 4y ago
      We can’t beat a Ferrari in a foot race either. I’m not sure this future is necessarily bad.
      - qualudeheart 4y ago
        It’s bad if you’re ruled over by a dictator who uses the AI to oppress you and you don’t have any rights. People worry about AI killing everyone but I think the AI panopticon angle is just as likely.
aurizon 4y ago
I think Alphafold will be of immense value. The article is too pessimistic. Alphafold will reveal a huge number of structural parameters that can be exploited by careful synthetic chemistry. You can know what base pair to change and see if that changes or blocks function. Things like Paxlovid can be tweaked towards an optimum. It is an analog to the way the Rosetta stock unlocked a few ancient languages. With this tool a huge number of testable structures will be amenable to tweaking, since we now have online ordering of almost any sequence. The tedious step will be the wet testing, but that has been solved by the multiple well test slides.We can now sequence and protein via dye/pore/emf methods. https://en.wikipedia.org/wiki/Nanopore_sequencing https://en.wikipedia.org/wiki/Nanopore_sequencing
- shakow 4y ago
  > I think Alphafold will be of immense value. Are you working in the field, or is that a dilettante's opinion?
  - aurizon 4y ago
    retired from the field
vcdimension 4y ago
This proposed improvement in the way RCT's are conducted could have a big impact on the speed of drug discovery: https://arxiv.org/pdf/1810.02876.pdf https://arxiv.org/pdf/1810.02876.pdf
aaron695 4y ago
Does AlphFold replace Foldit https://en.wikipedia.org/wiki/Foldit https://en.wikipedia.org/wiki/Foldit and Folding@home https://en.wikipedia.org/wiki/Folding@home https://en.wikipedia.org/wiki/Folding@home And if so, was this said about those projects? If not, why not?
sega_sai 4y ago
I'm not an expert in the topic by any means, but it feels to me like goal-post moving. Previously I had the impression they were saying 'protein folding' is the key problem. Now they say, so what we can fold proteins, there is more to drug discovery than this. I guess this is natural with any scientific advance.
- shakow 4y ago
  > but it feels to me like goal-post moving Because the two ‶they″ in your sentence are different people. Sure, for fundamental researchers, solving protein folding is a big thing – and although AlphaFold made strides in this direction, it didn't ‶solve″ it on any metric. But the other they, DD guys, already knew for decades that protein folding is but a brick in the whole process.
hedora 4y ago
So, the most recent paper says they get a "majority" of structures right: https://www.nature.com/articles/s41592-021-01362-6 https://www.nature.com/articles/s41592-021-01362-6 And the January paper from this team claimed 66%: https://www.nature.com/articles/s41592-021-01362-6 https://www.nature.com/articles/s41592-021-01362-6 This is impressive, but not game changing. Solving protein folding is analogous to solving the halting problem, but harder. The sequence is interpreted by squirting out a string of stuff that mutually attracts and repels itself and the environment. Different temperatures, neighboring proteins (the "program input"), etc, leads to different folds (the "program output"). As with software, there's a happy space of expected inputs where it's hopefully easy to reason about expected outputs. The difference between folding and the halting problem is that each step of the execution trace you'd need to generate to show a particular structure is reachable is quadratic in the number of atoms in the protein and surrounding environment, and the steps are not discrete. Alpha fold mostly just glues together previously seen folds that were produced via x-ray crystallography. This technique is called "threading" and is decades old. It has all the obvious limitations you'd expect if you extended my halting problem analogy. Also, most of the low hanging fruit in this field was picked a long time ago, so the 33% they perform poorly on is precisely the interesting part of the problem space. Edit: Here's an early paper from 1997: https://pubmed.ncbi.nlm.nih.gov/9237912/ https://pubmed.ncbi.nlm.nih.gov/9237912/ For whole structure threading, they get the correct fold "45-75% of the time". "Correct fold" is a weaker criteria than Alpha Fold uses in their abstract. However, it's interesting that Alpha Fold is still stuck in that range 25 years later. (Also, the 1997 paper is nowhere near state of the art.)
- jagraff 4y ago
  I disagree with your characterization. Of course, true protein folding computation is hard (literally NP-hard I believe) but the halting problem can be proven to be uncomputable, whereas protein folding is clearly computable - every time a protein is produced in our cells, protein folding is computed. Practically, the two may be the same, in the sense that we can neither truly compute how a complex amino acid sequence will fold nor whether a complex computer program will halt, but one is merely intractable, whereas the other is impossible.
j_m_b 4y ago
Protein structural data is the beginning of the problem, modeling the complex chemical reactions and interactions between proteins is where drug discovery happens.
sporkland 4y ago
I remember constantly hearing in the 90's and 00's that after dna sequencing was "solved" the next frontier was protein folding. Seems like we're getting there. What is the next frontier, or are people just being overly skeptical of alphafold?
deleted 4y ago
[deleted]
seydor 4y ago
The author makes it sound as if it's impossible to use reinforcement learning or some other technique to solve the ligand-bound conformations and automate the drug discovery process. Alphafold is probably just part 1 of the story.
jonrimmer 4y ago
The article mentions performing myriad assays as a rate-limiting step in drug discovery. My employer https://www.synthace.com https://www.synthace.com is trying to accelerate this process through software. We’ve created a platform to easily program liquid handling robots to perform complex protocols, and to do so using a Design-of-Experiments[1] approach to investigate multiple factors simultaneously. It’s early days for this approach but we’ve already had pharma customers using it to significantly accelerate their drug discovery process. [1] https://en.m.wikipedia.org/wiki/Design_of_experiments https://en.m.wikipedia.org/wiki/Design_of_experiments

203 points by panabee 4y ago

> Forming these coils, loops, and sheets is what proteins generally do, but ‘why?’ doesn’t enter into it. How do we know the model hasn’t figured out some of the ‘whys’ somewhere in there?

bigdict 4y ago
Because it learns a conditional distribution. It doesn't work on figuring out why the distribution is the way it is.
lrem 4y ago
Your question fundamentally falls into the area of unanswerable philosophy akin to "do insects feel pain?" But there's a reasonable intuition suggesting that the answer to your question is "no". What we're looking at is a non-linear regression model reproducing the function (which according to the article isn't really a function, but that's above both my and the model's knowledge) from a gene sequence to a 3d structure. It is heavily meta-optimised, so the "why's" would only be in the model, if reproducing the process of folding the protein was the cheapest way to guess the structure (). Intuitively it introduces at least one extra dimension, so should be way more expensive than finding analogues among known sub-aspects of the function. Hence, I would expect none of the "why's" to be in there. Sadly, if any insight for the "why's" was there after all, we don't have a method to extract it anyway. Disclaimer: I work in Google, far away from DeepMind, have no internal knowledge on this.
- salty_biscuits 4y ago
  "Sadly, if any insight for the "why's" was there after all, we don't have a method to extract it anyway." This has been my central frustration with working in ML. People always expect a "why" to exist, and by why I mean a cogent narrative explanation to complex phenomena. Maybe there is no "why" like this for a bunch of physical phenomena, maybe it is just a bunch of low level intricate stuff interacting in complex ways. There might be an emergent model that you can get a useful predictive model for with an ML model, then people get mad because the prediction doesn't solve the real meta problem that they were expecting to solve via the sub problem (e.g. solve folding then get mad because folding itself turns out not to be super useful because we don't know which protein to target, solve image classification then get mad because that doesn't make it easy to make a self driving car, etc, etc). "More is different" is definitely an idea in physics that needs to propagate into other fields to temper our expectations.
sgt101 4y ago
The "why" is a bit of an odd question anyway - the structure is as as the structure is. It's like asking why "red hears a galaxy", just words.
- freemint 4y ago
  Well, no. A bunch of mechanism have models of lower complexity that have almost exactly the same predictive power but a completely different structures. Those higher order structures are the "why". Why did does a cube on a inclined plane start to slide? You act like the correct answer is "because the subatomic particles and space time in the light cone of the experiment made it that way" when one should expect "because the sin of the incline angle times mass times local gravity became bigger then the static friction between a cos(incline angle) times the original cube weight and the surface at no incline" which is a lot simpler.
  - visarga 4y ago
    When the process being studied has more moving parts than humans can grasp in working memory there comes a limit after which we can't understand it anymore. Maybe the nice clean abstract concepts we expect are not there. It could be perfectly correct and yet complicated like front-end spaghetti code.
    - tsimionescu 4y ago
      That is possible, yes. But that doesn't make the question of "why?" meaningless, as the GGP was suggesting.
      - visarga 4y ago
        I see neural nets as extensions of human thought - they explore the fuzzy depths where we can't grasp directly, like a microscope or a telescope enhance vision. They are enhanced correlation engines.
        4y ago
pelorat 4y ago
It's probably not even possible for a human to understand the "why"

aabhay 4y ago

The author doesn’t answer the question. If not this, then what will? Because as far as I can tell, we’re nowhere near extracting the full value of AI-generated protein structures. Why plant this flag and be wrong later if you have no real idea of what should be done instead?

ChrisRackauckas 4y ago
The questions he asks here are exactly the questions that quantitative systems pharmacology (QSP) seeks to answer (and as a result, it's booming as a field). Just because you can build a drug to inactivate said protein doesn't mean you should. 85% of clinical trials fail as he states, and one of the main reasons why is because the target ends up being incorrect. Targeting some protein because a lot of it seems to exist when a given disease is occurring might end up targeting the symptom instead of the cause. Understanding how the complex systems interact, their feedbacks and their nonlinearities, is essential to knowing what needs to be targeted. We had already been able to quickly create new drug candidates, and with protein folding predictions we can now do that even faster. Those drugs can be tested in a lab to see if they bind to the proteins they're supposed to, and they keep getting quicker at hitting exactly the function they expected. But without making the billion dollar clinical trial more likely to be solving the actual problem, we're still going to be limited by "okay, so what in this pool of possible drugs should we risk trying next"? We can accurately knock out protein function, but we're still fishing in the dark when it comes to how to actually fix and regulate bodies.
echelon 4y ago
Because we have to be honest with ourselves. Don't tell the crystallographers they're no longer necessary for structure determination. If people flee the field and ML doesn't pan out, then we're worse off. Treat this as it is. An exciting approach that may help some now and yield fantastic results in the future. Don't count the chickens before they hatch. Even if the structures were entirely correct - and they're definitely not - there's a massive complex metabolome to figure out. Google is certainly milking the PR as much as they can, and that can be dangerous to the laymen approving research budgets.
- 4y ago

microSnowball 4y ago

I think alphafold gets hated on too much. It won’t revolutionize things but I bet people are out there right now looking at different structures and motifs only seen on alphafold to get a better idea on how existing drugs bind and affect them. And then designing analogues and so on. Time will tell, I guess. It’s kind of like anything in research, lots of small steps enable revolutionary breakthroughs every so often.

fabian2k 4y ago
You can assume that any known drug target has experimentally determined structures available, once you spend the enormous amounts of effort necessary to put a drug through real clinical trials the effort to determine the target structure is pretty much irrelevant. Of course there are plenty of drugs where we either don't know where they bind or we're probably wrong about where we think they bind. Or they bind at multiple places and some desirable or non-desirable effect are due to binding at places we don't know yet. There are real uses to having lots of high-quality structure predictions for proteins. Drug development is something that only get limited benefits here. If you want to know how drugs or drug candidates bind to proteins you first create a protein structure with X-ray crystallography. Then you soak your crystals with your drugs or drug candidates and determine even more structures. The interesting part here is not necessarily the overall fold of the protein (which is mostly what AlphaFold gives you) but e.g. a single hydrogen bond to the drug in the active pocket of the target protein. You need really high-quality data if you want to do any kind of rational drug design, most of the time we still just semi-randomly vary structures until they bind better as far as I understand.
epistasis 4y ago
I think it gets marketed too much and hated on too much. Given the utter dominance of Google advertising, I think the hating is a necessary counter in order to at least place it in its right place. Whatever skill Google has computationally is more than matched by their media dominance and public relations prowess.
- mtlmtlmtlmtl 4y ago
  I find this view very strange. If you apply the same logic to politics, the outcome is pretty grim. And we've been seeing more and more of that. I don't like hype or hate that's devoid of nuance. But actual scientists working in these fields don't generally pay attention to these things as much as we might. They read the papers, and they have years of training to help them decide what is overhyped and what isn't. I'm not sure what happens on HN or in advertising channels has such a huge bearing on this.

lrem 4y ago

I love this article. It nicely answers the question I posed (https://news.ycombinator.com/threads?id=lrem#32263287 https://news.ycombinator.com/threads?id=lrem#32263287) in the discussion of the original announcement: is today's db good enough to be a breakthrough for something useful, e.g. pharma or agriculture? And the answer, somewhat unsurprisingly, seems to be "useful, but not life-changing". And that's a perfectly good result in my eyes :)

frozencell 4y ago
OpenAI's GPT-3 and DALL•E2 might be life-changing for their creative users, writers and illustrators or beginner creators, I can't remember any life-changing use case for groups (outside of the creators themselves). For ML researchers, transformers seem to not be used as AGI at all (despite general or multi-modal potential) but mostly used for test and probability tool.
- p1esk 4y ago
  For ML researchers, transformers seem to not be used as AGI at all (despite general or multi-modal potential) but mostly used for test and probability tool. What do you mean?
  - frozencell 4y ago
    They are tools to help create the next tools.
- abrax3141 4y ago
  Life-changing like you mean by destroying creatives’ way of making a living?
SilasX 4y ago
That link goes to the top of your comment history. I think you want this link to ensure you see the right comment: https://news.ycombinator.com/item?id=32263287 https://news.ycombinator.com/item?id=32263287

xiphias2 4y ago

While the article is correct that knowing the protein structures in itself is not that interesting, it's a prerequisite step to predicting interactions between proteins, which is super interesting for drug discovery. What's encouraging is the rate of progress, not what has already been done.

ramraj07 4y ago
It’s not necessarily a pre-requisite: many of the more “exciting” drug platforms like Antibodies (which comprise like 90% of approvals nowadays anyways) in principle DONT NEED any structure info a priori. You don’t need to know a proteins structure to generate antibodies, just the protein itself to either inject into an animal or pass through a phage display column. Now typically drug companies will of course only work with proteins whose structure is known. Please note that the top drug companies have some of the best crystallography departments and they will most definitely get the structures done experimentally (if it’s possible). AlphaFold will likely help with that for sure. But it’s just one of those things that’s just a nice to have. If a protein is truly important and you cannot tell it’s structure you can still go ahead and make an antibody drug against it and Even characterize it using mutagenesis analyses.

dekhn 4y ago

To revolutionize drug discovery, you need to solve a number of problems that ML can't really address right now. We do not have well-formed theories of the molecular details of many diseases. There is no immediate computational approach that address this defect. The community has had fairly simplified models for some time, and there's a lot of historical belief that by knowing protein structures in details, we can understand the nature of a disease through its molecular etiology, and from that, we can make drugs (either small molecules or biomolecules) that modulate proteins in rational ways to eliminate the disease with a minimum of side effects. In my mind, much of the problem is similar to modern deep learning compared to previous techniques. Several extremely challenging problems (high accuracy voice recognition, image recognition, object detection) simple were not solvable through the statistical techniques and mental models adopted by the practitioners. It is not abundantly obvious that stupidly simple deep networks can be pretrained on enormous amounts of labelled data, or even unlabelled data, but we didn't even have the ability to know this confidently until we had the right network architectures, enough high quality labelled data, and adequate compute power to train them. I believe that by starting to think about disease modelling from the same mindset as deep learning (simple models with many parameters, the models don't actually represent the assumed mechanism, large amounts of high quality data, lots of CPU, GPU, and RAM) and also thinking of the disease treatment process in the same way will greatly increase our ability to "understand" and "treat" diseases, while knowing far less about their underlying mechanism that we thought. A common example is disease/patient stratification. If you've developed drugs that treat disease A, but it turns out later, there are really two diseases, A1 and A2 with different underlying mechanisms but superficially similar exterior symptoms, you'll realize why some percentage of your population didn't get better (and often got worse, given the underlying toxicity of some medicines). If we could just stratify diseases better, and classify patients into the right bins, the effectiveness of medicine will go up (and drugs will get through clinical trials faster/better). None of this addresses the later-stage issues, such as successfully running all the phases of a clinical trial and the other gauntlets you must pass in order to get a drug FDA-approved. I would continue to expect marginal improvements for the foreseeable future. But be aware: some companies already have managed to do a good enough job developing new medicines that they routinely create multi-billion-dollar blockbuster drugs year after year after year (my employer, Genentech, is a perfect example of that). It maintains an enormous and well-funded R&D arm that expends untold neurons attempting to understanding disease better even before we start to consider something as "druggable".

curious_cat_163 4y ago

Mapping DNA sequences to 3D protein structure is the problem that the AlphaFold tries to solve. I don't think it tries to solve for "drug discovery". I suspect that, like any ML problem, this one is a small part of the whole solution of drug discovery. There are always system-level dynamics at play. To me, some relevant questions before deciding to take on an ML problem tend to be: [x] Does solving it eliminate manual labor from the process? [x] Does it save $ in the progress towards solving the whole problem? [x] Is it fun to solve it?

pcrh 4y ago

This article makes some good points, but is incorrect on some others. In particular the statement " It is very, very rare for knowledge of a protein’s structure to be any sort of rate-limiting step in a drug discovery project!" does not reflect the realities of drug discovery. Knowing a protein's structure, and its structure when complexed with ligands/drugs, is a massively important bit of data in the armoury of medicinal chemists, which Derek Lowe knows all too well. Of course, it may be that knowing which protein to target is more important, and that problem isn't affected by AlphaFold.

zmmmmm 4y ago

This take seems to be somewhat over sceptical and slightly over-reaching to me. > when your entire computational technique is built on finding analogies to known structures, what can you do when there’s no structure to compare to Lots of people seem focused on the idea that deep networks can't do anything novel and are just like fancy search engines that find a similar example and copy it. This is not true. They do learn from much deeper low level structures in the domain they are exposed to. They can be aware of implicit correlations and constraints that are totally outside what may be recognised in the scientific understanding. Hence AlphaFold is quite capable of predicting a structure for which there is no previous direct "analogy". As long as the protein has to follow the laws of physics then AlphaFold as at least a basis to work from in successfully predicting the structure. > It is very, very rare for knowledge of a protein’s structure to be any sort of rate-limiting step in a drug discovery project! This and the following text are very reductive. It's like saying, back in 1945 that nuclear weapons would not be any sort of advantage in WW2 because it is very rare for weapons of mass destruction to win a war. Well yes it was rare, because they didn't exist. And so too did we not have a meaningfully accurate way to predict protein structures until AlphaFold. We've barely even begun to exploit the possible new opportunities for how to use that. And people have barely scratched the surface in adapting AlphaFold to tackle the related challenges downstream from straight up structure prediction. Predicting formation of complexes and interactions is the obvious next step and it's exactly what people are doing. It's not to say that it will revolutionise drug development, but the author's argument here is that he is confident it will not and he really doesn't assert much evidence of that.

ramraj07 4y ago
If you’re gonna get mad and quote a sentence to rail on the author, at the least quote the full sentence: the author ends it with “and there never will be.” Because among other things he’s talking about intrinsically disordered proteins[1]. What can the best prediction model do to predict the truly unpredictable? Just tell us that it’s unpredictable. And what is your second criticism exactly? The author comes from the drug discovery industry. What the author said should be generalized to: even if we know the perfect experimentally confirmed 1A resolution structure of every protein out there tomorrow, that won’t exactly revolutionize drug discovery. That’s because protein structure gives maybe 10% of the context you need to successfully design a drug. It’s dynamics, higher order interaction specifics, complex interplay in signaling pathways in particular cells in particular contexts and what entire cells and organ systems in THAT PARTICULAR ORGANISM do when this protein is perturbed, are what truly affects drug discovery. If you absolutely want to revolutionize DD, find us a better model to test things on that’s closer to the human body as a whole. Currently mice and rats are used and they’re not cutting it anymore. This fundamentally goes back to the downfall of the prototypical math or software guy trying to come and say “im gonna cure cancer with MATH!” No you’re not. You’re gonna help, and it’s appreciated, but if you’re gonna truly cure cancer you better start stomping on a few thousand mice and maybe also get an MD. 1. https://www.nature.com/articles/nrm3920 https://www.nature.com/articles/nrm3920

summerlight 4y ago

The modern world is not that simple enough to allow a single paper or technology to revolutionize anything. I don't understand why people are reiterating this obvious fact over and over? Most of the technological breakthroughs are usually a culmination of decades of research and investments.

evouga 4y ago
My observation is that breathless hype pieces proclaiming that a new technology will imminently revolutionize area X outnumber the articles expressing common-sense skepticism about the technology, by two to three orders of magnitude.
- yomkippur 4y ago
  lot of VCs have connection to media outlets, journals. its interesting that as VCs are imploding, we see more of the hype going away. somebody bet big on AlphaFold and OpenAI but realize they are not going to break even at least for another decade or two.
  - abrichr 4y ago
    > VCs are imploding, we see more of the hype going away. Do you have a source for either or both of these claims?

AlbertCory 4y ago

In the early 2000s, I took a bunch of UCSC Extension courses on mol bio, bioinformatics, and drug discovery. Back then, abundant DNA information was the thing revolutionizing the field. What the scientists (all from Roche) said was, more or less, "yeah, that helps a lot. It doesn't solve the whole problem, though." 20 years later they've gotten yet more help with Alphafold. Once again, they can do things faster, but it isn't a Moore's Law-type of change. It's still a really hard problem demanding culture, animal, and human tests, and those take time and money.

fny 4y ago
AlphaFold is in part impressive because a team with no protein design experience solved a challenge which had eluded experts for decades. It's absolutely a step change in demonstrating what's possible. The author can sit and criticize alpha fold from his armchair, but the dude is missing the forest for the trees. Do you remember how people on HN used to take comfort that AI couldn't write code? Now they're taking comfort in the fact that AI can't write code well. We're headed towards a word where data gets put in a blender to decide what to try next. That entire pipeline can and will be automated. And in that world, human ingenuity and expertise may become far, far less relevant.
- coldtea 4y ago
  >We're headed towards a word where data gets put in a blender to decide what to try next. That entire pipeline can and will be automated. And in that world, human ingenuity and expertise may become far, far less relevant. Let's hope we'll be dead by then...
  - osigurdson 4y ago
    We can’t beat a Ferrari in a foot race either. I’m not sure this future is necessarily bad.
    - qualudeheart 4y ago
      It’s bad if you’re ruled over by a dictator who uses the AI to oppress you and you don’t have any rights. People worry about AI killing everyone but I think the AI panopticon angle is just as likely.

aurizon 4y ago

I think Alphafold will be of immense value. The article is too pessimistic. Alphafold will reveal a huge number of structural parameters that can be exploited by careful synthetic chemistry. You can know what base pair to change and see if that changes or blocks function. Things like Paxlovid can be tweaked towards an optimum. It is an analog to the way the Rosetta stock unlocked a few ancient languages. With this tool a huge number of testable structures will be amenable to tweaking, since we now have online ordering of almost any sequence. The tedious step will be the wet testing, but that has been solved by the multiple well test slides.We can now sequence and protein via dye/pore/emf methods. https://en.wikipedia.org/wiki/Nanopore_sequencing https://en.wikipedia.org/wiki/Nanopore_sequencing

shakow 4y ago
> I think Alphafold will be of immense value. Are you working in the field, or is that a dilettante's opinion?
- aurizon 4y ago
  retired from the field

vcdimension 4y ago

This proposed improvement in the way RCT's are conducted could have a big impact on the speed of drug discovery: https://arxiv.org/pdf/1810.02876.pdf https://arxiv.org/pdf/1810.02876.pdf

aaron695 4y ago

Does AlphFold replace Foldit https://en.wikipedia.org/wiki/Foldit https://en.wikipedia.org/wiki/Foldit and Folding@home https://en.wikipedia.org/wiki/Folding@home https://en.wikipedia.org/wiki/Folding@home And if so, was this said about those projects? If not, why not?

sega_sai 4y ago

I'm not an expert in the topic by any means, but it feels to me like goal-post moving. Previously I had the impression they were saying 'protein folding' is the key problem. Now they say, so what we can fold proteins, there is more to drug discovery than this. I guess this is natural with any scientific advance.

shakow 4y ago
> but it feels to me like goal-post moving Because the two ‶they″ in your sentence are different people. Sure, for fundamental researchers, solving protein folding is a big thing – and although AlphaFold made strides in this direction, it didn't ‶solve″ it on any metric. But the other they, DD guys, already knew for decades that protein folding is but a brick in the whole process.

hedora 4y ago

So, the most recent paper says they get a "majority" of structures right: https://www.nature.com/articles/s41592-021-01362-6 https://www.nature.com/articles/s41592-021-01362-6 And the January paper from this team claimed 66%: https://www.nature.com/articles/s41592-021-01362-6 https://www.nature.com/articles/s41592-021-01362-6 This is impressive, but not game changing. Solving protein folding is analogous to solving the halting problem, but harder. The sequence is interpreted by squirting out a string of stuff that mutually attracts and repels itself and the environment. Different temperatures, neighboring proteins (the "program input"), etc, leads to different folds (the "program output"). As with software, there's a happy space of expected inputs where it's hopefully easy to reason about expected outputs. The difference between folding and the halting problem is that each step of the execution trace you'd need to generate to show a particular structure is reachable is quadratic in the number of atoms in the protein and surrounding environment, and the steps are not discrete. Alpha fold mostly just glues together previously seen folds that were produced via x-ray crystallography. This technique is called "threading" and is decades old. It has all the obvious limitations you'd expect if you extended my halting problem analogy. Also, most of the low hanging fruit in this field was picked a long time ago, so the 33% they perform poorly on is precisely the interesting part of the problem space. Edit: Here's an early paper from 1997: https://pubmed.ncbi.nlm.nih.gov/9237912/ https://pubmed.ncbi.nlm.nih.gov/9237912/ For whole structure threading, they get the correct fold "45-75% of the time". "Correct fold" is a weaker criteria than Alpha Fold uses in their abstract. However, it's interesting that Alpha Fold is still stuck in that range 25 years later. (Also, the 1997 paper is nowhere near state of the art.)

jagraff 4y ago
I disagree with your characterization. Of course, true protein folding computation is hard (literally NP-hard I believe) but the halting problem can be proven to be uncomputable, whereas protein folding is clearly computable - every time a protein is produced in our cells, protein folding is computed. Practically, the two may be the same, in the sense that we can neither truly compute how a complex amino acid sequence will fold nor whether a complex computer program will halt, but one is merely intractable, whereas the other is impossible.

j_m_b 4y ago

Protein structural data is the beginning of the problem, modeling the complex chemical reactions and interactions between proteins is where drug discovery happens.

sporkland 4y ago

I remember constantly hearing in the 90's and 00's that after dna sequencing was "solved" the next frontier was protein folding. Seems like we're getting there. What is the next frontier, or are people just being overly skeptical of alphafold?

deleted 4y ago

[deleted]

seydor 4y ago

The author makes it sound as if it's impossible to use reinforcement learning or some other technique to solve the ligand-bound conformations and automate the drug discovery process. Alphafold is probably just part 1 of the story.

jonrimmer 4y ago

The article mentions performing myriad assays as a rate-limiting step in drug discovery. My employer https://www.synthace.com https://www.synthace.com is trying to accelerate this process through software. We’ve created a platform to easily program liquid handling robots to perform complex protocols, and to do so using a Design-of-Experiments[1] approach to investigate multiple factors simultaneously. It’s early days for this approach but we’ve already had pharma customers using it to significantly accelerate their drug discovery process. [1] https://en.m.wikipedia.org/wiki/Design_of_experiments https://en.m.wikipedia.org/wiki/Design_of_experiments