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AlphaFold: Using AI for scientific discovery
- pplonski86 8y agoThe ranking of the methods is here: http://predictioncenter.org/casp13/zscores_final.cgi?formula=assessors http://predictioncenter.org/casp13/zscores_final.cgi?formula... - the improvement is very similar to ImageNet improvement when deep neural networks were successfully used in image recognition comparing to 'traditional' methods.
- John_KZ 8y agoScary. I never thought I'd hate to see protein folding being solved.
- chii 8y agowhy is it scary?
- alexgmcm 8y agoIt's weird that people seem incapable of being optimistic about scientific and technological progress unlike say in the 50's where it seemed many genuinely believed we would explore space and have robots etc. Now people just try to malign AI, genetic engineering, nuclear power etc.
- jf- 8y agoIt’s not unreasonable to be cautious. Nuclear accidents did happen, which is what lead people to fear nuclear power, even if it is safer than the public realises. Not barging full speed ahead into unknown technologies with large downside potential is only sensible.
- ogrisel 8y agoThe public is also afraid of nuclear technology because of nuclear weapons used to destroy the cities Hiroshima and Nagasaki with a single bomb each time.
- snovv_crash 8y agoI would argue that the weaponization of nuclear power, and a generation growing up with nuclear bomb drills, was what drove the fear of nuclear. Before that people were putting it in their toothpaste.
- baq 8y agothe danger is when greed takes over e.g. as can be seen with social networks - an useful tool being milked in such a way that makes societies crack at their foundations. custom design of proteins could be used to treat and cure most diseases, but curing doesn't earn as much money and treating.
- jcims 8y agoThere seems to be a trend of indulging in oppressive fantasy. Maybe it balances out any irrational exuberance about the arrival of new technological frontiers. Or maybe some folks are just grumpy.
- taneq 8y agoNew science is always scary, and the better it works the scarier it is.
- placebo 8y agoIt is scary for legitimate reasons. It doesn't mean we should stop the advancement of science, but ignoring human tendencies to leverage the best of science to amplify the destructive abilities of worst in human nature is a bit on the naive side. Whether we are wise enough to handle our intelligence is still an open question.
- kaveh_h 8y agoGene technology has potential to develop truly targeted weapons. What's worse is that it could spread through anyone but only attacking it's intended target(s).
- chii 8y ago> Gene technology has potential to develop truly targeted weapons. What's worse is that it could spread through anyone but only attacking it's intended target(s). so, no collateral damage? Targeted assassinations? This is the CIA's wetdream.
- Numberwang 8y agoWhat do you mean solved and what do you mean scary?
- KidComputer 8y agoIt's not solved.
- John_KZ 8y agoIt's being solved. Given how important this is, we'll have very good models fairly soon.
- ghani 8y agoAwesome!
- jf- 8y ago> Our team focused specifically on the hard problem of modelling target shapes from scratch, without using previously solved proteins as templates. I have a feeling they’re going after the biologics market with this. Predict structure directly from DNA sequences, simulate affinities, then make a batch and test in-vitro. Throw in a loop to feed back data to make a better DNA sequence. Definitely heading down the road to automated protein design.
- ramraj07 8y agoPredicting protein structure de novo is still a long way from actual drug discovery. You can. Generate antibodies much fasterif all you want is some protein that binds your target anyways
- jf- 8y agoThat’s just minimising the accomplishment, predicting structure need not be the end of the process for it to be a significant advance.
- natechols 8y agoIt's not minimizing the accomplishment - it's an impressive technical achievement, but every new advance in computational modeling for the last several decades has been touted as a revolutionary step forward for drug discovery, and every time it turns out to be an incremental improvement at best. Actually producing a marketable drug is far more difficult than simulating how two molecules fit together.
- timr 8y agoNo, you're exaggerating. I work in the field. Ab initio structure prediction (what this is) is an interesting technical challenge, but has little to no direct impact on biologics (or any other kind of) drug discovery. The tools and technologies sometimes end up translating but that's a long-term process.
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- xvilka 8y agoIs this able to solve all pending Folding@Home tasks?
- comepradz 8y agoPossibly not as in new FahCores. They might be useful in conjunction to each other, as AlphaFold is useful for protein structure prediction, Folding@home can confirm the predicted structure through simulation.
- TomMarius 8y agoIs the confirmation significantly faster or is the same amount of work needed?
- cing 8y agoThe majority of ongoing Folding@Home tasks are not aimed at structure determination, but rather simulating the conformational dynamics of folded proteins (exploring the energy landscape rather than searching for the global minimum). Very few of the CASP algorithms are well-suited for this problem.
- melling 8y agoI imagine there are hundreds, if not thousands, of scientific problems that DeepMind could help with. It's just a matter of getting people working on the problems.
- wrinkl3 8y agoI wonder if they're gonna collab with Neuralink. Among all of Musk's startups those two are poised to synergize the most.
- forgotmyhnacc 8y agoDeepmind is owned by alphabet, not Elon musk.
- wrinkl3 8y agoMixed up Deepmind and OpenAI again, my bad.
- Cybiote 8y agoThe blog is light on details but it appears Alpha is being used for branding here. AlphaFold seems to depart in a significant way from the perfect information game playing agent's architecture. A few questions spring to mind: 1) What is the architecture of the generative network and where exactly does it fit in the pipeline? 2) What is the interaction with the database? Is there an encoder being trained with real sequences further augmented with variations using the generative network? 3) What is the structure of the neural network that encodes the sequence? Is it a graph network, LSTM or simple conv-net? 4) The gradient descent step is very vague. Is it a physically based differentiable model (not a neural network) whose parameters are being optimized with gradient descent using automatic differentiation? Or something else? In short, there's some detail on scoring but how are the proposals being generated? Questions aside, the results speak for themselves and are head and shoulders above all other showings. I wonder what it feels like for someone whose been in the field for years. Despite the high score, there's still a long way to go before results reach real world utility. It's also worth keeping in mind that from a systems biology perspective, protein folding is only a small part of what makes getting clinically useful results difficult. I might have missed something but I could not find anywhere indications of an intention to publish further details. That would be disappointing if such were indeed the case.
- DrNuke 8y agoI would question them more bluntly on the accountability issue though: the scientific community needs full reproducibility of exceptional results these days more than ever, so they should just make an effort on that front imho. They do not need to open source their code and their data in full, just planning a peer reviewed, open demonstration may suffice?
- 317070 8y agoThe contest IS an open demonstration? You get unlabeled data, need to guess the labels and after you submit the labels the external comitee tells you how good you were. At this point it might be unreproducible, but the paper is about it being verified and demonstrated.
- cjohansson 8y agoIt seems like this AI makes probabalistic guesses based on empirical data, so you need to validate the guess by comparing it with reality. Sometimes reality is not available and from my ignorant perspective, a much better way would be to discover the mathematical principles behind protein-folding and deductively know the exact folding with certainity. Focusing resources on a deductive solution to the problem is much better than creating a statistical guessing machine. AI solutions often seem to shift focus from deductive solutions to statistical ones and I don't think that is progress in the longterm
- jf- 8y agoIt’s the only progress we’re likely to get on mathematically intractable problems, until maybe a few generations into quantum computing.
- opless 8y agoThe hopes pinned on quantum computing seem to be like hoping holistic medicine cures cancer.
- jf- 8y agoCorrect me if I’m wrong, but that reads to me as thoughtless eye rolling cynicism. Is there a basis to it? Does quantum computing not offer our best shot at solving currently unsolvable problems?
- tylerhou 8y agoIt isn't even clear whether the class of problems quantum computers can efficiently solve is a strict superset of the class of problems classical computers can efficiently solve, before you even consider whether it's clear that we can build quantum computers with any reasonable number of logical qbits: https://en.wikipedia.org/wiki/Quantum_supremacy https://en.wikipedia.org/wiki/Quantum_supremacy
- Jach 8y ago
- sytelus 8y agoThis is a fantastic achievement. About 8 years ago I'd an interesting conversation with a friend on how any material with arbitrary desired properties can be constructed using proteins. You can build something as hard as turtle's shell or as soft as jellyfish. You can build liquids that dissolves plastics or you can build the most flexible fibers known in the universe. One way to think about proteins is generalized parametrized materials. If you knew the inverse function of properties -> protein structure, it would change the world far beyond dollar value of the invention. The DNA mechanism is evolution's best effort yet to build exactly that. This was such a beautiful insight that I remember rushing to Amazon and getting few books to understand basics of protein folding. The subject has some extremely beautiful foundational simplicity that is easy to understand but it quickly gets complex enough that it would be hard to navigate without interdisciplinary mind melds. With this new progress, hope is that protein engineering through AI would get huge boost in community attention and more accelerated progress!
- random_moonwalk 8y agoAny particularly good books you can recommend that you came across on your Amazon binge?
- 7373737373 8y agoI can recommend Engines of Creation: 1.0 summary: http://web.mit.edu/cortiz/www/3.052/3.052CourseReader/3_EnginesofCreation.pdf http://web.mit.edu/cortiz/www/3.052/3.052CourseReader/3_Engi... 1.0: http://e-drexler.com/d/06/00/EOC/EOC_Table_of_Contents.html http://e-drexler.com/d/06/00/EOC/EOC_Table_of_Contents.html 2.0: https://web.archive.org/web/20140810022659/http://www1.appstate.edu/dept/physics/nanotech/EnginesofCreation2_8803267.pdf https://web.archive.org/web/20140810022659/http://www1.appst...
- Symmetry 8y agoI highly recommend Engines 2.0 rather than the original. Drexler's conception of nanobots before he had done the heavy lifting of working out the designs that showed up in Nanosystems probably aren't very practical compared to more factory assembly line structures. Thermal motion is just so much easier to deal with in an enclosed tube than at the end of a wobbly arm.
- Invictus0 8y agoTheir methods don't sound particularly groundbreaking, the achievement seems to be in the implementation. I hope people don't jump to the conclusion that AI can do science because there isn't actually any experimentation here, just a statistical analysis of a few key amino acid properties. I hope that they will be able to abstract some formulas or rules about protein folding from the mess of statistics. I imagine that having a rulebook would be much more efficient than using AI, because protein folding isn't so much a game of chance as it is an extremely convoluted puzzle.
- brootstrap 8y agoThis is my major gripe as well. Brute forcing isnt science. It's data analysis and machine learning. Not science. I dont know if i will ever trust an AI to do 'science' for me to be quite honest. This just seems like a marketing ploy to get more venture funding. Hey now we are't just playing games, we are doing real science. Please donate 5 billion dollars so we can keep running up our AWS bill. These science models wont train themselves!!?!
- natechols 8y agoThe rulebook approach is basically what groups like David Baker's have been doing for years. However actually applying the rulebook is a difficult problem; I expect AI would actually prove much more efficient in the end, especially with optimized hardware.
- glenrivard 8y agoThis is a major accomplishment and shocked not getting more press.
- derangedHorse 8y agoThis was a pretty well written article. It was slightly technical, but still accessible to the average lay-person.
- brootstrap 8y agoIs this science , or machine learning disguised as science to try and appear more useful?
- kinchen 8y agoWhat's the path from protein folding to 'new potential within drug discovery?'
- klmr 8y agoIt’s a fairly direct path, since computational drug discovery relies heavily on protein folding predictions.
- cing 8y agoThe path is most likely through reliable structure prediction of drug targets. That would open up rational drug design projects that may have previously been impossible. The only problem is that experimental structure determination is so good in pharma, that it's hard to compete. For example, on a structure-enabled project, it may be possible to experimentally solve multiple high-resolution 3D models per week with an order of magnitude higher accuracy than predicted models. Once you can routinely get structures, there's still the rest of the drug discovery pipeline left to go.
- burning_hamster 8y ago> experimentally solve multiple high-resolution 3D models per week I thought this was only true if you already have a structure; otherwise you typically run into the [phase problem](https://en.wikipedia.org/wiki/Phase_problem https://en.wikipedia.org/wiki/Phase_problem), which is often a significant hurdle. But I haven't done much biochemistry in years, so there might be better approaches than MAD/MIR/etc. that make the phase problem a non-issue.
- kinchen 8y agoIf you already have the structures, what is there left to solve?
- kinchen 8y agoAh, interesting. How do you experimentally verify that you've solved the 3D model? My first guess is that you can have an agent bind to targets with that structure and then figure out how much binding occurred. This is a very uneducated stab, though.
- syntaxing 8y agoIs there something equivalent but for medicine? Essentially AI assisted medicine generation. If not, does anyone know what the field name is (I thought it was molecular biology but I might be wrong)?
- klmr 8y agoYou might be interested in computer-aided drug design [1], in particular computational target prediction. It’s a fairly big field (both in academic research and in commercial application), and not exactly new. This is technically a subfield of molecular biology but that term is extremely broad. [1] https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design https://en.wikipedia.org/wiki/Drug_design#Computer-aided_dru...
- sjg007 8y agoYes. People are interested in protein-drug interactions (protein ligand in general) as well as drug drug interactions... Small molecule structures (now helped by a fast cryoMR procedure). I bet the fast cryoMR stuff will be adapted to proteins too so we can get very good deep NN ML models in the very near future.
- jfarlow 8y agoThat kind of thing is generally thought of as machine-learning, or AI assisted "Drug Discovery", but there's not a great name for the field at large. Because there are so many different kinds of 'medicine' at this point, there are a number of companies that use similar tools for various definitions of medicine: Small Molecules: http://www.twoxar.com/ http://www.twoxar.com/ & http://atomwise.com/ http://atomwise.com/ Antibodies: http://www.antiverse.io/ http://www.antiverse.io/ Multi-Domain proteins: https://serotiny.bio https://serotiny.bio (my company)
- stdplaceholder 8y agoI wonder if team “zhang” is angry. They’re way ahead of the rest of the entrants and would have had an impressive result, if they hadn’t been blown out of the water by A7D.
- deleted 8y ago[deleted]
- lawrenceyan 8y agoIt's great that computational protein design is finally reaching a point where our computational and algorithmic capabilities have made it possible to actually start implementing legitimate real potential products. This is what people like D.E. Shaw Research, David Baker's Lab, Vijay Pande's Lab, and countless others have spent the better part of their lives on, and I'm incredibly excited for what we can and will achieve with this technology.
- buboard 8y agoHow about they scale this up to predict human phenotypes from DNA sequences.
- assblaster 8y agoDeriving phenotype from genomics is partly a problem of data. To give a computer the best chance to definitively correlate phenotype with sequence, you need millions to tens of millions to hundreds of millions of DNA sequences from unrelated individuals from every possible genetic lineage, and you'd also need objective analysis of phenotypes without cultural influence, ie someone who is boisterous in one culture would be normal in another culture, independent of the actual genetics. Computationally, this is statistical analysis, and I doubt that AI would be able to offer anything unique. Protein folding prediction, on the other hand, is more of a question of "where do you start to arrive at the answer most efficiently", and AI is well suited for this, and it would be much better than humans at prediction using methodologies and correlations far outside of human brain capability.
- buboard 8y agook. but how about just faces? i wonder if it is possible to find enough data for that
- sjg007 8y agoYes, some people do this e.g. extract a plausible face from DNA.
- assblaster 8y agoIt is hard enough finding the similarities between children and parents. The assumption of face phenotyping is that you could be able to roughly guess what someone will look like based on their grandparents, which seems unlikely for an AI to accomplish, and humans are designed evolutionarily to assess subtleties in faces to identify friends and family from foe.
- atomical 8y agohttp://blogs.sciencemag.org/pipeline/archives/2018/12/03/the-latest-on-protein-folding http://blogs.sciencemag.org/pipeline/archives/2018/12/03/the... > But protein folding is far from a solved problem, fear not. XKCD’s take on this remains accurate! It’s going to be very interesting indeed to see the progress over the next few years in this area, but that progress is not going to be the discovery of some general solution. It’s going to be a mixture (as mentioned above) of better understanding of the physical processes involved, larger databases of reliable experimental data covering more structural classes, and faster/more efficient ways for searching through all these (both the possible structures and the real ones) and generalizing rules to tell us when we’re closing in on something accurate.
- breatheoften 8y agoReading the description of the submission process is pretty interesting: http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf. There were multiple submissions and manual synthesis of different model results in some cases. I’m curious how this competition works exactly — it seems like a set of label predictions are submitted and some form of accuracy result feedback is provided (a single accuracy score for the whole prediction set?). And that there are a certain number of allowed submissions ...? How much of the ultimate strategy for playing this game at a high-level ends up being around optimizing for receiving as much leaked information from the test set as possible — is best guess at this point that this result is likely to be a good indicator of a true increase in prediction capability ...?
- kxs 8y agoThe targets are completely unknown. They were experimentally solved, but not submitted to the Protein Data Bank. You basically get a target every day (meaning sequence of amino acids) and then depending on the category, you have three days to predict it (I think upto two weeks for "human" servers). In the modeling category they participated in, you can submit 5 models. A model is a fully predicted 3d structure of the protein. The targets are mostly independent. Some targets were split over a few days. But in principle, a target on day X has no connection to a target on day Y. The targets have varying difficulty. There are two categories: TBM (templated based) and FM (free modeling). You don't know which protein corresponds to which category, you can just guess by looking at the available template data. They focused on FM targets. Meaning there are no homologous available. It's hard to say how good of an indicator the results are. Looking at the contact prediction results, many methods are getting very good at constructing MSAs (gathering similar sequences). We already saw this at CASP12 - I think the FM targets are getting "easier" in that sense. There is basically zero feedback throughout the whole competition. Some targets are released after the deadline (because of publications), but in general, you don't know anything until the CASP meeting, which currently takes place. The competition ended in August.
- YetAnotherNick 8y agoThere are multiple neural network models which looks like they are aiming to do the same thing. I wonder deepmind won just because of computation power and expertise in training the neural networks or they come up with much better and different model than other participants.
- grondilu 8y agoIs there any chance this could be used for materials science instead of drug discovery? I personally doubt the world needs more drugs.
- dekhn 8y agoA long-running joke at CASP was that eventually, a neural network would be trained that could predict structures as well as Alexey Murzin. Alexey is the guy who would routinely "win" CASP by explaining how to memorized the sequence on a 6 months before from a preliminary structure poster at some random conference (he also helped create SCOP).
- YetAnotherNick 8y agoHow does it compare with foldit top entries? I thought semi guided way is the best way by far to solve this.
- nie 8y agoI have wrote an intro to the problem for lay-person. To the best of my ability. I hope this will be helpful for some of you[1]. [1] https://blog.nilbot.net/2018/12/pipeline-protein-structure-prediction/ https://blog.nilbot.net/2018/12/pipeline-protein-structure-p...
- MakeDLgr8again 8y agoAny data available to create an open source version?
- mikhailfranco 8y agoTangential, but great interview with Demis Hassabis here: https://www.bbc.co.uk/sounds/play/p06qvj98 https://www.bbc.co.uk/sounds/play/p06qvj98 (skip to 01:30 for real start)