
AlphaFold: Using AI for Scientific Discovery - panabee
https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery
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lucidrains
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)

~~~
faitswulff
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-
casp...](https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-
just-happened/)

~~~
lucidrains
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

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aabhay
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
> 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
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.

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rutherf0rd
The best take on AlphaFold:
[https://moalquraishi.wordpress.com/2018/12/09/alphafold-
casp...](https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-
just-happened/)

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

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elil17
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
It probably has something to do with the parent company being named Alpha_Bet.

~~~
elil17
Damn you're right

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emmelaich
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.

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xvilka
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/)

