Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

AlphaFold: a solution to a 50-year-old grand challenge in biology

deepmind.com

Post reply on HN
Print view View original

351–360 of 683 posts

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#351

Post by vadansky »

vadansky wrote: post #128

Just to add to this whole "It's not solved! Yes it is!" discussion. Note that >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 so…

Just to add to my own comment. Why does HN like being so pedantic about the definitions of words? This is an interesting post regarding AI and cellular biochemistry. Do we really need to add a philosophical debate about the meaning of "solution"? Personally I think anyone who can't add to the discussion about AI and protein folding should just not comment, instead of settled on adding to the what does solution mean "debate". I'd love to see a blanket rule flagging pedantic posts.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#352

Post by WanderPanda »

dekhn wrote: post #335
mjn wrote: post #308

Earlier quoted context omitted.

Even if you try to account for the overall R&D cost, DeepMind isn't that large an organization by the standards of biomedical research. It's very big and well funded for a computer science research organization, yes, and most CS departments can't match its resources. But the NIH budget is $40 billion, and private pharmaceutical companies do another $80 billion in annual R&D. It's interesting that this kind of breakth…

DeepMind is taking advantage of NIH's funding. For example, Anfinsen who demonstrated that proteins fold spontaneously and reproducibly ( https://en.wikipedia.org/wiki/Anfinsen%27s_dogma ) ran a lab at NIH. Levinthal (who postulated an early and easily refutable model of protein folding) was funded by NIH for decades. Most of the competitors at CASP are supported by NIH and its investments have contributed to the mod…

It seems like spending these government funds on creating new challenges like CASP and ImageNet could have an enormous ROI. Don’t let them try to choose the winner, just let them define the game

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#353

Post by xyzzyz »

harperlee wrote: post #12

Not 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.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#354

Post by papaf »

amelius wrote: post #274

Curious, what are the sizes of the training and validation/test datasets (number of structures)?

Curious, what are the sizes of the training and validation/test datasets (number of structures)?

The proteins are shown on the CASP website [1]. Both the number of residues and number of proteins are bigger than I expected.

[1] https://predictioncenter.org/casp14/targetlist.cgi

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#355

Post by whimsicalism »

alquemist wrote: post #339
whimsicalism wrote: post #5

Earlier quoted context omitted.

Progress like this was, in my view, inevitable after the invention of unsupervised transformers. It'll be genetics next. e: although AlphaFold appears to be convolutionally based! I suspect that'll change soon.

FWIW, transformers is to sequences what convnets is to grids, modulo important considerations like kernel size and normalization. Think of transformers as really wide (N) and really short (1) convolutions. Both are instances of graphnets with a suitable neighbor function. Once normalization was cracked by transformers, all sort of interesting graphnets became possible, though it's possible that stacked k-dimensional…

I work in the field, I don't need the difference explained to me.

> Think of transformers as really wide (N) and really short (1) convolutions

Modern transformer networks are not "really short" and you're also conflating the difference between intra- and inter- attention.

There is still a pitched battle being waged between convnets and transformers for sequences, although it looks like transformers have the upper hand accuracy wise right now, convnets are competitive speed-wise.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#356

Post by patagurbon »

kordlessagain wrote: post #138
Zenst wrote: post #130

Earlier quoted context omitted.

Even without any improvement, the amount of grunt-work the AI can pre-do and get down to a short-list - that in itself will see changes in progress speeding research up.

> and get down to a short-list There's no reason to believe the list will contain all solutions, however.

No but it will hopefully contain some. Which for many if not most problems is all that matters

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#357

Post by Odenwaelder »

xphos wrote: post #346

Like 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…

I have no idea what you are talking about.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#358

Post by pmastela »

candiodari wrote: post #21
harperlee wrote: post #12

Not 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?

This will allow us to discover much more about the structure of the cell (of "life") at a before this unprecedented speed. We should find many, many more mechanisms and targets for medicine, but it takes 10-20 years to bring a new medicine to market. So in 5 years you'll see exactly zero new medicines pop up.

I agree. The main inhibitor of speed that products of this advancement will be deployed at will likely be determined by local policies. Though, given just how profound some of the impacts on medicine might be, the speed at which they can be deployed might become a matter of national security (a healthier population bodes well for a healthier economy which in turn strengthens national security). Hopefully this competition shortens the time-to-market for all these new medicines.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#359

Post by [deleted] »

dekhn wrote: post #343
mjn wrote: post #341

Earlier quoted context omitted.

That's a good point; this system certainly didn't come from nowhere! The protein datasets they used also mostly came out of various NIH-funded projects. What I meant to focus on was that I think DeepMind has less of a pure money/scale advantage in this area than in some others. In something like Go or Atari game-playing, there are many academic groups researching similar things, but their resources are laughably smal…

Personally I think a major part of the secret sauce is Google's internal compute infrastructure. When I was an academic, 50% of my time went to building infra to do my science. At Google, petabytes of storage, millions of cores, algorithms, and brains were all easily tappable within a common software repo and cluster infrastructure. That immediately translates to higher scientific productivity.

[deleted]

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#360

Post by ducttapecrown »

jeffbee wrote: post #53

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.

How much would the labor cost, though?
Post reply on HN