AlphaFold: a solution to a 50-year-old grand challenge in biology • Hacker News Board

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

#661

Post by softwaredoug » Wed, Dec 02, 2020, 3:11 AM UTC

PaulDavisThe1st wrote: post #657

softwaredoug wrote: post #648

Earlier quoted context omitted.

This is exactly my point. There is something rotten in the research labs where society is not getting return on investment for basic research. If the goal is to just produce researchers that can work at corporate research labs, then I feel we could get more bang for the buck. If the goal is to do move research in the public good, something needs to change. Maybe it’s the fact there’s too little money out there, and i…

The problem is that people in academia and outside of it were saying the same thing in the 70s, 80s, 90s, 00's ... so if you really want to make the claim that "society is not getting return on investment for basic research" , you need to claim with a straight face that this also applies to the last 50 years of academic basic research (at least). Alternatively, explain what has changed and when.

Citing increases in fraud and retractions, this article makes the case that as funding has increased, scientific quality has worsened. (Which is counter to what I guessed above!) Pursuit of scientific knowledge for its own sake replaced by obsession with grant cycles.

https://www.jamesgmartin.center/2020/01/the-intellectual-and...

But lots of other things have also changed that may or may not be causes:

- many fewer tenure track positions

- many more jobs in industry that require or value a 4 year or advanced degree

- obtaining a college degree as a right of passage seen as increasingly essential to you g adulthood

- the rise of data science: more jobs in industry that have access to lots of data and demand scientific rigor

- the rise of private cloud supercomputing (is deep mind) vs, say, a public university's cluster

- the obsession in some foreign countries of getting advanced degrees from American universities, creating essentially a guest visa workforce that is easily abused

- rise of Big Tech which has money to throw at things like protein folding

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

#662

Post by jvolkman » Wed, Dec 02, 2020, 4:59 AM UTC

dang wrote: post #515

kovek wrote: post #470

Earlier quoted context omitted.

I've seen you mention this [More] comment a few times now. I like it, though what if you change the design of the More functionality?

Yep, the intention is to change the design by getting rid of it. HN used to just render entire threads in one go, and once we release some performance improvements we hope to do that again.

In the meantime, how about just placing the More link at the top of the comments section in addition to the bottom so it stands out better?

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

#663

Post by heycosmo » Wed, Dec 02, 2020, 5:29 AM UTC

flobosg wrote: post #453

heycosmo wrote: post #446

Earlier quoted context omitted.

> I don't think I fully understood this, but I'll give it a shot anyway. If your artificial sequence aligns with others, there's a chance that it will fold like them, depending on the quality and accuracy of the multiple sequence alignment. Since multiple sequence alignments are built under the assumption of homology (all sequences have a common ancestor), it's a matter of how far from the "sequence sampling space" y…

Oh, I see! Yes, an intrachain alignment of an artificial sequence does not by itself give any information about co-evolution, especially since you don't know whether your protein is actually folding. To assess co-evolution you need a multiple sequence alignment between protein homologs containing correlated mutations. > I understand that similar sequences may fold similarly (although as length increases, I highly dou…

> To assess co-evolution you need a multiple sequence alignment between protein homologs containing correlated mutations.

That makes sense. So in the CASP competition, when teams are given a sequence, do their algorithms do something like the following?

1. Search database for homologs of given sequence 2. Look at MSA and correlated mutations of homologs 3. Look for similar correlated mutations in given sequence

I imagine 1-3 could somehow be embedded in a NN after training on a protein database.

> What do you mean by "proximal"? Close in space, or similar in structure?

I mean close in space.

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

#664

Post by C4stor » Wed, Dec 02, 2020, 8:17 AM UTC

softwaredoug wrote: post #642

C4stor wrote: post #629

Earlier quoted context omitted.

The deepmind research team is essentially all PhD though, so it seems academia isn't doing such a bad job.

Specifically the linked article wonders about the research environment of academia compared to industry. Why teams of hundreds in academia with their own super computing resources couldn’t make the same advances. He posits there’s something not great going on about how academic research environments make advances, the poor incentive structures, the abuse and burnout of PhDs, the lack of open sharing of findings, the…

Academia is doing a lot of advances every year. The fact it didn't make _this one_ is not really relevant to postulate that academia is inefficient.

It happens that the team at deepmind is apparently pretty damn good at deep learning problems, so they're going faster than matching academia labs.

It's not to say that academia has none of the problems you mentioned, but it's imo unreasonable to expect that, in a world where both public and private labs exist, only public ones would make advances.

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

#665

Post by im3w1l » Wed, Dec 02, 2020, 8:23 AM UTC

cs702 wrote: post #118

Two years ago, after DeepMind submitted its first set of predictions to CASP (Critical Assessment of protein Structure Prediction), Mohammed AlQuraishi, an expert in the field, asked, "What just happened?" https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp... Now that the problem of static protein structure prediction has been solved (prediction errors are below the threshold that is considered acceptable i…

> Now that the problem of static protein structure prediction has been solved (prediction errors are below the threshold that is considered acceptable in experimental measurements)

This seems premature. Even though it does very well on average, there may be some areas where it struggles, and those areas may turn out to be important.

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

#666

Post by twic » Wed, Dec 02, 2020, 10:04 AM UTC

softwaredoug wrote: post #487

cs702 wrote: post #118

Two years ago, after DeepMind submitted its first set of predictions to CASP (Critical Assessment of protein Structure Prediction), Mohammed AlQuraishi, an expert in the field, asked, "What just happened?" https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp... Now that the problem of static protein structure prediction has been solved (prediction errors are below the threshold that is considered acceptable i…

> I don’t think we would do ourselves a service by not recognizing that what just happened presents a serious indictment of academic science. Much like other fields, I do begin to question the academic structure to making advances. It appears something is rotten in the state of academia. Oddly it's academia doing incremental improvements to existing methods but industry making novel leaps and bounds... The other majo…

The academic groups working on this have a tiny fraction of the resources that Google do.

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

#667

Post by SiempreViernes » Wed, Dec 02, 2020, 11:24 AM UTC

softwaredoug wrote: post #487

cs702 wrote: post #118

Two years ago, after DeepMind submitted its first set of predictions to CASP (Critical Assessment of protein Structure Prediction), Mohammed AlQuraishi, an expert in the field, asked, "What just happened?" https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp... Now that the problem of static protein structure prediction has been solved (prediction errors are below the threshold that is considered acceptable i…

> I don’t think we would do ourselves a service by not recognizing that what just happened presents a serious indictment of academic science. Much like other fields, I do begin to question the academic structure to making advances. It appears something is rotten in the state of academia. Oddly it's academia doing incremental improvements to existing methods but industry making novel leaps and bounds... The other majo…

The article then goes on to describe a not very general set of circumstances

> But in part due to the canonicalization of CASP, protein structure prediction effectively has a two-year clock cycle, where separate research groups guard their discoveries until after CASP results are announced.

and further noting

> As I discussed earlier, it is clear that between the Xu and Zhang groups enough was known to develop a system that would have perhaps rivaled AlphaFold.

Finally, and rather crushingly for your thesis, is the points made about the real industrial groups:

> What is worse than academic groups getting scooped by DeepMind? The fact that the collective powers of Novartis, Pfizer, etc, with their hundreds of thousands (~million?) of employees, let an industrial lab that is a complete outsider to the field, with virtually no prior molecular sciences experience, come in and thoroughly beat them on a problem that is, quite frankly, of far greater importance to pharmaceuticals than it is to Alphabet. It is an indictment of the laughable “basic research” groups of these companies, which pay lip service to fundamental science but focus myopically on target-driven research that they managed to so badly embarrass themselves in this episode.

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

#668

Post by raxxorrax » Wed, Dec 02, 2020, 12:02 PM UTC

softwaredoug wrote: post #661

PaulDavisThe1st wrote: post #657

Earlier quoted context omitted.

The problem is that people in academia and outside of it were saying the same thing in the 70s, 80s, 90s, 00's ... so if you really want to make the claim that "society is not getting return on investment for basic research" , you need to claim with a straight face that this also applies to the last 50 years of academic basic research (at least). Alternatively, explain what has changed and when.

Citing increases in fraud and retractions, this article makes the case that as funding has increased, scientific quality has worsened. (Which is counter to what I guessed above!) Pursuit of scientific knowledge for its own sake replaced by obsession with grant cycles. https://www.jamesgmartin.center/2020/01/the-intellectual-and... But lots of other things have also changed that may or may not be causes: - many fewer…

I think while funding has been increased overall due to more people in academia, the processes to acquire said funding got more complex so that a significant part of work is actually making sure the next grant can be secured. Similar situation in publishing. The research might only get a backseat.

That said, this is primarily a computational problem, so the advances here might not be applicable to basic research.

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

#669

Post by krull10 » Wed, Dec 02, 2020, 1:09 PM UTC

softwaredoug wrote: post #487

cs702 wrote: post #118

Two years ago, after DeepMind submitted its first set of predictions to CASP (Critical Assessment of protein Structure Prediction), Mohammed AlQuraishi, an expert in the field, asked, "What just happened?" https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp... Now that the problem of static protein structure prediction has been solved (prediction errors are below the threshold that is considered acceptable i…

> I don’t think we would do ourselves a service by not recognizing that what just happened presents a serious indictment of academic science. Much like other fields, I do begin to question the academic structure to making advances. It appears something is rotten in the state of academia. Oddly it's academia doing incremental improvements to existing methods but industry making novel leaps and bounds... The other majo…

Industry is not going to fund the overwhelming majority of research areas in biology, physics, chemistry, mathematics, etc. Data science and AI are an exception, where people in industry are much better paid, and can get access to much better resources that would be hard to afford in academia... It’s not surprising this type of advance came from an industry funded group. On the other hand, it is academia and its structure that has enabled so many other discoveries, for example, Crispr DNA tech, our understanding of gravitational waves, or the proof of the Poincaré conjecture.

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

#670

Post by krull10 » Wed, Dec 02, 2020, 1:14 PM UTC

softwaredoug wrote: post #642

C4stor wrote: post #629

Earlier quoted context omitted.

The deepmind research team is essentially all PhD though, so it seems academia isn't doing such a bad job.

Specifically the linked article wonders about the research environment of academia compared to industry. Why teams of hundreds in academia with their own super computing resources couldn’t make the same advances. He posits there’s something not great going on about how academic research environments make advances, the poor incentive structures, the abuse and burnout of PhDs, the lack of open sharing of findings, the…

I would say that is most likely due to a massively higher salary and no teaching responsibilities. Academia can’t compete on salary with industry in AI / data science.