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.
AlphaFold: Using AI for Scientific Discovery
11–20 of 23 posts
Re: AlphaFold: Using AI for Scientific Discovery
#12For 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 , which seems to outperform Alphafold with a very concise architecture. Code and model is at https://github.com/gjoni/trRosetta
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...
Re: AlphaFold: Using AI for Scientific Discovery
#13For 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 , which seems to outperform Alphafold with a very concise architecture. Code and model is at https://github.com/gjoni/trRosetta
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...
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
Re: AlphaFold: Using AI for Scientific Discovery
#14For 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 , which seems to outperform Alphafold with a very concise architecture. Code and model is at https://github.com/gjoni/trRosetta
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...
The author of the article you linked also has this repo, to welcome the public to start training https://github.com/aqlaboratory/proteinnet , following in the same veins as Imagenet
Re: AlphaFold: Using AI for Scientific Discovery
#15I hope, it will help other folding research projects too, like Folding@Home[1], Rosetta@Home[2].
Re: AlphaFold: Using AI for Scientific Discovery
#16A 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 t…
> 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"?
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.
Re: AlphaFold: Using AI for Scientific Discovery
#17The best take on AlphaFold: https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...
Re: AlphaFold: Using AI for Scientific Discovery
#18A 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 t…
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.
Re: AlphaFold: Using AI for Scientific Discovery
#19A 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_"
"Alpha" means dominant, the best. The best chess player, the best go player...
Re: AlphaFold: Using AI for Scientific Discovery
#20A 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 t…
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