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Title as submitted is hyperbole, please fix?
I agree. "AlphaFold achieves a median score of 87.0 GDT". While this is a major advance, to me 100 GDT would be 'solved', not 87.
Are you a domain expert? Because:
> According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods.
This is a big step forward, but the outstanding question as far as to whether or not this is useful for evaluating novel proteins, is going to be how good is the confidence metric at telling the user to trust or not trust the results. You can see from their examples, that AlphaFold is very good but not perfect. I imagine for some proteins it will still give misleading or erroneous results and if you can’t tell when t…
Earlier quoted context omitted.
We can already determine how a few proteins (170k — which sounds like a lot, but which is only 0.09% of all currently-catalogued protein sequences) fold by experimental work. What an accurate model of protein folding allows us to do, is to take our big database of DNA, predict protein foldings for all of it, and then stand up a search index for this database, keying each amino-acid "row" by the "words" of its predict…
Considering that this system "uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks" to determine a single protein structure, making predictions for all proteins encoded in a human genome seems impractical at this stage. With luck, this advance will help lead to discovery and definition of new folding rules and optimizations that will make protein folding predictions for the wh…
Earlier quoted context omitted.
Art's a smart guy and I have a lot of respect for his biological intuition, but his understanding of computational biology is very limited.
I would imagine that he is not assessing this advancement merely using his own personal expertise, but rather the combined expertise of the resources he represents. CEOs don't just look at problems and potential solutions. They have people who look at those things, and then tell them their opinion. In any case, you've picked a nit with one of the three people quoted. Any objections to the other two?
This sounds wonderful and frightening. On the one hand, now we can engineer drugs at light speed. But wasn't protein folding supposed to be NP-hard? Can deep learning find the cracks in P vs NP? Perhaps making clever guesses at prime factors because it learned some weird structural fact that has eluded mathematicians. If we break crypto, there goes the modern world. Banks, bitcoin, privacy, Internet, the whole sheban…
This “uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks”. That is a moderate amount of hardware for this kind of work, so it seems they have a more efficient algorithm.
Also, this algorithm doesn’t solve protein folding in the mathematical sense; it ‘just’ produces good approximations.
Pretty interesting that they only used about $15k worth of resources (retail price) to achieve this. It's not a technique that would have been out of reach for other organizations based only on not being able to afford the compute.
This is a big step forward, but the outstanding question as far as to whether or not this is useful for evaluating novel proteins, is going to be how good is the confidence metric at telling the user to trust or not trust the results. You can see from their examples, that AlphaFold is very good but not perfect. I imagine for some proteins it will still give misleading or erroneous results and if you can’t tell when t…
> the outstanding question as far as to whether or not this is useful for evaluating novel proteins That is not an outstanding question. The test on which DeepMind scored high marks is a test of how well the algorithm folds novel proteins -- proteins whose ground-truth structure has not yet been published.
Earlier quoted context omitted.
We can already determine how a few proteins (170k — which sounds like a lot, but which is only 0.09% of all currently-catalogued protein sequences) fold by experimental work. What an accurate model of protein folding allows us to do, is to take our big database of DNA, predict protein foldings for all of it, and then stand up a search index for this database, keying each amino-acid "row" by the "words" of its predict…
Considering that this system "uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks" to determine a single protein structure, making predictions for all proteins encoded in a human genome seems impractical at this stage. With luck, this advance will help lead to discovery and definition of new folding rules and optimizations that will make protein folding predictions for the wh…
This sounds wonderful and frightening. On the one hand, now we can engineer drugs at light speed. But wasn't protein folding supposed to be NP-hard? Can deep learning find the cracks in P vs NP? Perhaps making clever guesses at prime factors because it learned some weird structural fact that has eluded mathematicians. If we break crypto, there goes the modern world. Banks, bitcoin, privacy, Internet, the whole sheban…
Far from an expert on complexity theory, but NP-hard problems can be approximated in polynomial time. With Deep Learning you are doing approximation. So this is nothing ground breaking in that respect.
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.