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

deepmind.com

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

#421

Post by t_serpico »

tonfa wrote: post #226
Seanambers wrote: post #179

Earlier 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…

> 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.

Also, pharma does not really have a huge incentive to work on this problem. Solving the protein folding problem does not automatically translate to new drugs just in the same way CRISPR or DNA sequencing did not. It's another tool in the toolbox (which to be clear is a big deal).

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

#422

Post by garmaine »

sdenton4 wrote: post #393
garmaine wrote: post #333

Earlier quoted context omitted.

> Are there any cases where ML performed decently but then hit a ceiling while expert systems kept improving? Yes, this describes entire history of AI including several boom-bust cycles. In particular the 80's come to mind. Yes the practitioners think that there's no technical barriers stopping them from eating the world, but that's exactly what people thought about other so-called revolutionary advances. Although to…

I think the disconnect this time around is in productionization. We're getting breakthroughs in a wide range of problems, and translating those gains in the problem space into 'real' stable, practical solutions we can use in the world is the remaining gap, and often takes years of additional effort. It's still really expensive to launch this stuff, and often requires domain expertise that the ML research team doesn't…

Next time you fly through a busy airport, think about the system which assigns planes to gates in realtime based on a large number of variable factors in order to maximize utilization and minimize waits. This is an expert system design in the 80's and which allowed a huge increase in the number of planes handled per day at the busiest airports.

Or when you drive your car, think about the lights-out factory that built-it, using robotics technologies developed in the 80's and 90's, and the freeways which largely operate without choke points again due to expert system models used by city planners.

These advances were just as revolutionary before, and people were just as excited about AI technologies eating the world. Still, it largely didn't happen. To continue the example of robotics, we don't have an equivalent of the Jetson's home robot Rosey. We can make a robot assemble a $50,000 car, but we can't get it to fold the laundry.

These rapid successes you see aren't literally "any problem from any field" -- it's specific problems chosen specifically for their likely ease in solving using current methods. DeepMind didn't decide to take on protein folding at random; they looked around and picked a problem that they thought they could solve. Don't expect them to have as much success on every problem they put their minds to.

No, machine learning is not trivially solving the hardest problems in every field. Not even close. In biomedicine, for example, protein folding is probably one of the easiest challenges. It's a hard problem, yes, but it's self-contained: given an amino acid sequence, predict the structure. Unlike, say, predicting the metabolism of a drug applied to a living system, which requires understanding an extremely dense network of existing metabolic pathways and their interdependencies on local cell function. There's no magic ML pixie dust that can make that hard problem go away.

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

#423

Post by jeffxtreme »

GDT_TS for AlphaFold is now comparable is at experimental levels; but that's based on the class of proteins for which we've been able to determine the 3D structure of the protein, for which there might be selection bias.

I wonder if we can determine if this extends to proteins that aren't as keen to determining their 3D structure?

For example, certain proteins are more crystallizable than others.. For these non-crystallizable proteins, I wonder if we can say that AlphaFold would generate accurate 3D models? And if possible, might there be a way to map out this uncertainty?

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

#424

Post by tim333 »

andy_ppp wrote: post #348

So, sorry to be a philistine but what specific discoveries will this lead to... will it make it easier to produce antivirals or even molecular machines?

"DeepMind said it had started work with a handful of scientific groups and would focus initially on malaria, sleeping sickness and leishmaniasis, a parasitic disease" https://www.theguardian.com/technology/2020/nov/30/deepmind-...

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

#425

Post by dluan »

I worked in the lab that helped develop folding@home, as well as the game where the crowd was the chaotically trained machine that folded and unfolded one amino acid at a time. This feels like a pretty significant new chapter in the humanity movie.

A few times, I get immense pangs of jealousy for younger people a generation or a half before me. And I'm only 30! This is one of those times.

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

#426

Post by t_serpico »

dalke wrote: post #403
_RPL5_ wrote: post #377

Earlier quoted context omitted.

The industry process will not change. You still need industrial biologists to generate and validate AphaFold structures, interpret the results as part of the bigger picture, and to finally design the drugs. And, then, of course you still need to validate the drugs in experimental systems (first the test tube, then mice, then humans). So your second guess is correct - one of the steps is much cheaper now, which margin…

> "armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein" Back in the 1990s, when I worked on structure data, I remember that at least some crystallizations were easy enough they could be done as a rotation project. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287266/ suggests that life is now a lot easier than the 1990s. Quoting the abstract: > Macromolecular…

Also, one important thing to realize is that AlphaFold was trained largely on proteins that we were able to crystallize. I'd be very curious to see how its performance fares as a function of 'ease of crystallization'.

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

#427

Post by Invictus0 »

zaroth wrote: post #319
zaroth wrote: post #224

Earlier quoted context omitted.

> The price of training the final architecture is meaningless. The research is the giant shoulders you stand on, the compute cost is the price of the tool you need to do the present-day work. Both are relevant but the shoulder’s of giants are generally more accessible, particularly if we’re talking about published research and not proprietary tech. A competing team is not starting from the same place the DeepMind tea…

To expand on this, after fully reading AlQuraishi's "What Just Happened" post from a couple years ago, was this point that he made; > I don’t think we would do ourselves a service by not recognizing that what just happened presents a serious indictment of academic science. There are dozens of academic groups, with researchers likely numbering in the (low) hundreds, working on protein structure prediction. We have bee…

I don't think AlQuraishi really hits the mark in his critique. The mere fact that hundreds or thousands of people working on a problem for decades doesn't account for the fact that the field of machine learning has been growing extremely rapidly over the last decade, the compute power available has grown exponentially, and the people working on the problem simply weren't looking at the problem in the way that the deepmind people were looking at it.

If you were trying to get across the Atlantic, this would be like getting upset at a group of bridgebuilders for trying to solve the problem by building a bridge across instead of by inventing the airplane. The approaches are that different.

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

#428

Post by flobosg »

heycosmo wrote: post #412

Fascinating! AlphaFold (and other competitors) seem to use MSA (Multiple Sequence Aligment) and this (brilliant) idea of co-evolving residues to build an initial graph of sections of protein chain that are likely proximal. This seems like a useful trick for predicting existing biological structures (i.e. ones that evolved) from genomic data. I wonder (as very much a non-biologist), do MSA-based approaches also help u…

> do MSA-based approaches also help understand "first-principles" folding physics any better?

Not really. MSA-based approaches, as most structure prediction methods, have as a goal to find the lowest energy conformation of the protein chain, disregarding folding kinetics and basically all dynamic aspects of protein structure.

> If I write a random genetic sequence (think drug discovery) that has many aligned sequences, without the strong assumption of co-evolution at my disposal, there does not seem any good reason for the aligned sequences to also be proximal.

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" your sequence is located compared to the others.

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

#430

Post by gfodor »

dekhn wrote: post #54
elevenoh wrote: post #48

So the median accuracy went from ~58% (2018) to 84% (2020) in 2 years? Does 84% == solved? Also, any low hanging frut implications for longevity tech?

100% accuracy is "solved".

You can't get 100% accuracy on something for which you don't or can't know the ground truth.
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