5 ms·

AlphaFold: Using AI for Scientific Discovery

136 points by panabee 7y ago

aabhay 7y ago
A colleague of mine in chemistry gave his thoughts on AI for protein folding recently: “things keep getting better, but they’re nowhere close to being good”. I think part of the issue at play here is the cost of confirming success, not simply the cost of generating a plausible solution. In most domains of AI that have shown success, the cost of confirmation is trivial (look at the image and check the label) whereas the cost of generating a plausible solution was high. Like many AI fields, I believe that the real breakthrough will not be a direct approach, but an approach the solves the most pressing barrier to using AI in the first place.
- bobosha 7y ago
  > but an approach the solves the most pressing barrier to using AI in the first place. what do you think is the "most pressing barrier to using AI"?
  - ethanbond 7y ago
    From my perspective, in the life-sciences space, it’s certainly the lack of high quality data. To be more specific, there’s tons of data, but it’s sloppy, lossy, and non standardized. Many of those discussing the promise of near future AI are either academics or new to the field. The real data landscape is so much worse than they would imagine it is.
  - aabhay 7y ago
    The cost of confirming success. If we can decrease the cost of going from theoretical protein to physical test, then I think the AI part will be quickly solved.
- TheGallopedHigh 7y ago
  I think the point here is slightly missed. Protein folding simulations are accurate ok hydro all models where brute force is used to explore the parameter space. The AI models could narrow that space and then employ the stricter models. Therefore I don’t think the cost here is great.
- deleted 7y ago
  [deleted]
elil17 7y ago
A bit off topic, but what the hell does the word "Alpha" mean in their marketing? Are they just going to call every AI they create from now on "Alpha_"
- woadwarrior01 7y ago
  It probably has something to do with the parent company being named Alpha_Bet.
  - elil17 7y ago
    Damn you're right
- gwern 7y ago
  AlphaStar didn't really use any techniques from AlphaGo either, so the answer is 'yes'. Anything sufficiently whizzy will get the 'Alpha' prefix for branding.
- klhweropiu 7y ago
  "Alpha" means dominant, the best. The best chess player, the best go player... https://en.wikipedia.org/wiki/Alpha_(ethology) https://en.wikipedia.org/wiki/Alpha_(ethology)
- jeffshek 7y ago
  Alpha is also a reference to the "learning rate" in machine learning models.
  - atorodius 7y ago
    Isn’t that usually lambda?
sanxiyn 7y ago
Eagerly waiting for CASP14 in 2020. CASP is a biennial competition. AlphaFold participated in CASP13 in 2018.
lucidrains 7y ago
For those interested, it appears as though David Baker (who dedicated his life to protein folding) has also turned to deep learning. His lab recently published https://www.biorxiv.org/content/10.1101/846279v1 https://www.biorxiv.org/content/10.1101/846279v1, which seems to outperform Alphafold with a very concise architecture. Code and model is at https://github.com/gjoni/trRosetta https://github.com/gjoni/trRosetta
- deleted 7y ago
  [deleted]
- faitswulff 7y ago
  Do you know if he turned to deep learning after AlphaFold's stunning performance at CASP13 in 2018? I haven't heard anything from that particular niche since the "AlphaFold @ CASP13: “What just happened?”" blog post: https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...
  - lucidrains 7y ago
    It absolutely did. Both AlphaFold and RaptorX (another deep learning approach from Jinbo Xu's lab) were mentioned in that paper as the main motivation of their architecture
  - lucidrains 7y ago
    The author of the article you linked also has this repo, to welcome the public to start training https://github.com/aqlaboratory/proteinnet https://github.com/aqlaboratory/proteinnet , following in the same veins as Imagenet
emmelaich 7y ago
Amazing. The difference between their number one position and the the second is a greater difference than any other two consecutive positions. Not an especially important metric, but impressive nonetheless.
xvilka 7y ago
I hope, it will help other folding research projects too, like Folding@Home[1], Rosetta@Home[2]. [1] https://foldingathome.org/ https://foldingathome.org/ [2] https://boinc.bakerlab.org/rosetta/ https://boinc.bakerlab.org/rosetta/
rutherf0rd 7y ago
The best take on AlphaFold: https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...

136 points by panabee 7y ago

A colleague of mine in chemistry gave his thoughts on AI for protein folding recently: “things keep getting better, but they’re nowhere close to being good”. I think part of the issue at play here is the cost of confirming success, not simply the cost of generating a plausible solution. In most domains of AI that have shown success, the cost of confirmation is trivial (look at the image and check the label) whereas the cost of generating a plausible solution was high. Like many AI fields, I believe that the real breakthrough will not be a direct approach, but an approach the solves the most pressing barrier to using AI in the first place.

bobosha 7y ago
> but an approach the solves the most pressing barrier to using AI in the first place. what do you think is the "most pressing barrier to using AI"?
- ethanbond 7y ago
  From my perspective, in the life-sciences space, it’s certainly the lack of high quality data. To be more specific, there’s tons of data, but it’s sloppy, lossy, and non standardized. Many of those discussing the promise of near future AI are either academics or new to the field. The real data landscape is so much worse than they would imagine it is.
- aabhay 7y ago
  The cost of confirming success. If we can decrease the cost of going from theoretical protein to physical test, then I think the AI part will be quickly solved.
TheGallopedHigh 7y ago
I think the point here is slightly missed. Protein folding simulations are accurate ok hydro all models where brute force is used to explore the parameter space. The AI models could narrow that space and then employ the stricter models. Therefore I don’t think the cost here is great.
deleted 7y ago
[deleted]

elil17 7y ago

A bit off topic, but what the hell does the word "Alpha" mean in their marketing? Are they just going to call every AI they create from now on "Alpha_"

woadwarrior01 7y ago
It probably has something to do with the parent company being named Alpha_Bet.
- elil17 7y ago
  Damn you're right
gwern 7y ago
AlphaStar didn't really use any techniques from AlphaGo either, so the answer is 'yes'. Anything sufficiently whizzy will get the 'Alpha' prefix for branding.
klhweropiu 7y ago
"Alpha" means dominant, the best. The best chess player, the best go player... https://en.wikipedia.org/wiki/Alpha_(ethology) https://en.wikipedia.org/wiki/Alpha_(ethology)
jeffshek 7y ago
Alpha is also a reference to the "learning rate" in machine learning models.
- atorodius 7y ago
  Isn’t that usually lambda?

sanxiyn 7y ago

Eagerly waiting for CASP14 in 2020. CASP is a biennial competition. AlphaFold participated in CASP13 in 2018.

lucidrains 7y ago

For those interested, it appears as though David Baker (who dedicated his life to protein folding) has also turned to deep learning. His lab recently published https://www.biorxiv.org/content/10.1101/846279v1 https://www.biorxiv.org/content/10.1101/846279v1, which seems to outperform Alphafold with a very concise architecture. Code and model is at https://github.com/gjoni/trRosetta https://github.com/gjoni/trRosetta

deleted 7y ago
[deleted]
faitswulff 7y ago
Do you know if he turned to deep learning after AlphaFold's stunning performance at CASP13 in 2018? I haven't heard anything from that particular niche since the "AlphaFold @ CASP13: “What just happened?”" blog post: https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...
- lucidrains 7y ago
  It absolutely did. Both AlphaFold and RaptorX (another deep learning approach from Jinbo Xu's lab) were mentioned in that paper as the main motivation of their architecture
- lucidrains 7y ago
  The author of the article you linked also has this repo, to welcome the public to start training https://github.com/aqlaboratory/proteinnet https://github.com/aqlaboratory/proteinnet , following in the same veins as Imagenet

emmelaich 7y ago

Amazing. The difference between their number one position and the the second is a greater difference than any other two consecutive positions. Not an especially important metric, but impressive nonetheless.

xvilka 7y ago

I hope, it will help other folding research projects too, like Folding@Home[1], Rosetta@Home[2]. [1] https://foldingathome.org/ https://foldingathome.org/ [2] https://boinc.bakerlab.org/rosetta/ https://boinc.bakerlab.org/rosetta/

rutherf0rd 7y ago

The best take on AlphaFold: https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...