First Nobel prize for AI from this?
Hopefully they give one out for this, if only so I can say I'm a Nobel Prize contributor
No way, you were on the team? Congrats.
AlphaFold: a solution to a 50-year-old grand challenge in biology
381–390 of 683 posts
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#382This sounds big, like really really big. At least from my old times providing my idle computing resources to Folding@Home and following that project, this seems like the major golden milestone for protein folding.
Exactly what I was thinking. In a very small way many of us tried to help with this problem back in the day. Makes it feel even more important.
Now I'm waiting for the equivalent news about SETI@Home ;-)
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#383Earlier quoted context omitted.
> So it wasn't out of reach for academia, pharmaceuticals, or others with a bit of resources. How much does hiring a deepmind-like team cost though? (massively more than the TPU resources?) Still within reach of pharmaceutical industry I guess, but maybe not so easy for academia.
From what I can gather, Google bought Deepmind for 500 million USD in 2014, they have outstanding debt to its parent company as of 2019 of 1.3 billion USD. And they had income around 100 million in 2019 but it's all against Google, so looks like a 2 billion +/- 0.5 operation so far, and who knows if they pay for compute. Other articles place the runrate at 500 million per year in 2019. Which means 500 million * 6 yea…
That's the cost of running DeepMind as a whole, right? Which includes all the other stuff they've worked on, like games.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#384Earlier quoted context omitted.
I have a related question about this. If experimental methods produce results around a score of 90, what is the baseline we are comparing the DeepMind results against? If the experimental error is equal to the observed DeepMind error, how can we say which one is actually more erroneous?
Finding the energy of each configuration should be much easier than finding the lowest-energy configuration. Can that be calculated ab-initio or it is still too expensive?
The problem with ab-initio methods in this context is the sheer number of non-covalent interactions present in these large proteins. A simple protein would require a hybrid quantum mechanic/molecular mechanics simulation to even approximate the vibrational energy required to validate equilibrium.
These proteins are so massive that we often use Daltons [1] as an averaged measure of molecular weight.
Conceptually one of the most promising applications of quantum computing is theoretical chemistry, and we are only now starting to make progress in this avenue [2]. I anticipate it would require quantum computing to explicitly optimise large folded proteins.
1. https://en.m.wikipedia.org/wiki/Dalton_(unit) 2. https://arxiv.org/abs/2004.04174
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#385Like this is awesome and a huge advancement but one thing that worries me with an AI solution is that it doesn't really draw us any closer to the why. Why do proteins fold the way they do? We can predict the resulting structure which is extremely significant, we have no clue why. While we get the insight of being able to predict some structures we don't get the insight of why things are happening the way they are. In…
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Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#386Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?
My friend, who is working in crystallization lab, has told me that she’s gonna be claiming unemployment soon, and she was only half joking.
She can still work on complexes, binding modes, and engineered biomolecules (eg, protein–drug conjugates and antisense oligonucleotide dimers) where the training data isn't really there.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#387So it might "be over" for small molecules, but let's see large macromolecules and protein assemblies be predicted
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#388Earlier quoted context omitted.
I particularly like the rant on pharmaceuticals companies lack of basic research. My impression has been that medical progression have been slow for quite some time, nice to see that there are some truth to that. In the end software and tech companies might just eat up the pharmaceutical industry as well. - It's all just code at some level. The Deepmind team did this with ; "We trained this system on publicly availab…
Yeah, it was a big slap in the face. But, to be fair, most of the scientific and technological advances (sequencing efforts, structural genomics projects, etc.) that generated the data used by DeepMind came from academia and, to a lesser extent, the pharma industry.
I think the lesson here is that most of the big data genomic, metabolic, pharmacologic and other research will all be driven by deep learning. The models themselves however require 100+ gpus so we are sort of back in that phase where you need large compute systems to even compete. A single lab will have issues unless they can leverage a cloud and then also get grant funding to spend that money on the cloud compute... which may be difficult b/c its basically a consumable now and you don't have any hardware leftover.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#389Earlier quoted context omitted.
I mean, credit where credit is due. Google employs some of the greatest names in artificial intelligence and the DeepMind team had a huge chunk of them working on this problem. While the resources may have been available, I don’t think any other single institution had the level of brain power.
It also makes one reconsider the notion that monopolies are entirely bad. This essentially appears to be a vanity project for Google. Though of course they'll benefit from it in many ways, but it's not like they're doing this as the core product of their service. It's a pretty awesome achievement.
Look at all of the incredible things that came out of Bell labs during their monopolistic reign. I think a better way to put it is not all monopolies are bad for research and progress but many are bad for other social and economic reasons. Like any position of power, it depends on how it is used snd who is using it.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#390Earlier quoted context omitted.
I agree. The failures have to be explicable if we are to trust a model.
Doesn't it depends on the application ? i.e. some applications can tolerate false positives/negatives ?
May well be, but if you spend more compute, and human time checking for those corner cases than if you went with another, more consistent exhaustive search algorith, then the method looses to it economically.
This is more the case the more close to bruteforce you come, like encryption cracking. Imagine, spending years of HPC cluster time, trying to break a password, while knowing you have a single digit chance to miss the right key, in a way which would be completely impossible with with a conventional solution.