121–130 of 683 posts
Earlier quoted context omitted.
Based on the going rate of a 32-core TPUv3 slice ($32/hr USD) running "for a few weeks", isn't this closer to $65k USD?
It says $1,752/mo for v3-8, so I just multiplied it 8x.
Does it mean there is no point in playing fold.it anymore?
Considering the resource requirements for this AI approach mentioned in the article, its unlikely that its been tested on more than a few tens to hundreds of proteins. This may only work on a subset of the proteome so I would think it worth it to continue playing if you find it to be a fun past-time.
First Nobel prize for AI from this?
Earlier quoted context omitted.
It's an improvement- and a big one- but not a solution to the problem. It mainly shows just how stuck the community had gotten with their techniques and how recently improvements in DNNs and information theory methods can be exploited if you have lots of TPU time.
It’s officially recognized as a solution.
Now onto the much harder problem of doing the reverse: taking an arbitrary structure and determining an amino-acid sequence that will fold into it.
What for?
The reverse folding problem lets you specify a structure and then make a genetic sequence to produce it. For example you could look at this virus to see how it infects its host, then design a custom protein to act as an anti-body stopping it, which is a capability we don't currently have.
Forward folding is certainly useful, but reverse folding would be revolutionary.
Earlier quoted context omitted.
It's an improvement- and a big one- but not a solution to the problem. It mainly shows just how stuck the community had gotten with their techniques and how recently improvements in DNNs and information theory methods can be exploited if you have lots of TPU time.
It’s officially recognized as a solution.
>According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods.
So if we go by >= 90 as solved:
>In the results from the 14th CASP assessment, released today, our latest AlphaFold system achieves a median score of 92.4 GDT overall across all targets.
they solved for their targets, but
>Even for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT (data available here).
They basically admit they still haven't "solved" it for "most challenging free-modelling category"
Take that as you will, not sure how useful the ">= 90 is solved" criteria is since they call it "informal" themselves.
CASP (Critical Assessment of protein Structure Prediction) is calling it a solution. To quote from the article: "We have been stuck on this one problem – how do proteins fold up – for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment." --Professor John Moult C…
"To see DeepMind produce a solution for this" does not imply something is solved. I can produce a bad solution. I can produce a really good solution. All without solving a problem.
Sometimes announcements like this are a bit over-the-top. But what really, to me, cements the 'big-deal' of this is the "Median Free-Modelling Accuracy" graph half way down the page. Scores of 30-45 for 15 years. Now scores of 87-92. This isn't a minor improvement, it's a leap forward.
Not to mention the fact that two years ago they took it from 45% to >60%. If they can continue improving, even with an exponential decay in rate of improvement, this is certainly a stunning example of technological disruption.