81–90 of 98 posts
So, unsurprisngly, it appears that applying a transformer to multiple sequence alignments extracts somewhat more spatial information about proteins than we had been able to previously squeeze out. It's pretty clear at this point that the work led to a large improvement in psp scores, but there's literally nothing else groundbreaking about it; I don't mean that in a bad way, except to criticize all the breathless pres…
Earlier quoted context omitted.
> DeepMind claimed that they used “128 TPUv3 cores or roughly equivalent to ~100-200 GPUs”. Although this amount of compute seems beyond the wildest dreams of most academic researchers... So, we're talking like what? Maybe $100K to $300K of hardware? Wet biology labs often have multiple pieces of $100K+ equipment at their disposal. Why shouldn't computational labs too?
Yeah but it's also putting it together and properly utlizing it, which takes specialist knowledge.
The cost of computing will also go down in the future and for government funds this sounds like a drop in the sea when they are building multi billion dollar particle accelerators.
To me the most interesting part of the article is the cometary on where basic research is going to happen in the future. The fear is that if it only happens in large companies, then the unbiased pool of experts society relies on will be smaller and less informed. Along with the issue of nobody being around for the slog of defining a field, setting up databases, competitions and standards. These are what allow well fu…
> DeepMind claimed that they used “128 TPUv3 cores or roughly equivalent to ~100-200 GPUs”. Although this amount of compute seems beyond the wildest dreams of most academic researchers... So, we're talking like what? Maybe $100K to $300K of hardware? Wet biology labs often have multiple pieces of $100K+ equipment at their disposal. Why shouldn't computational labs too?
To me the most interesting part of the article is the cometary on where basic research is going to happen in the future. The fear is that if it only happens in large companies, then the unbiased pool of experts society relies on will be smaller and less informed. Along with the issue of nobody being around for the slog of defining a field, setting up databases, competitions and standards. These are what allow well fu…
> DeepMind claimed that they used “128 TPUv3 cores or roughly equivalent to ~100-200 GPUs”. Although this amount of compute seems beyond the wildest dreams of most academic researchers... So, we're talking like what? Maybe $100K to $300K of hardware? Wet biology labs often have multiple pieces of $100K+ equipment at their disposal. Why shouldn't computational labs too?
To me the most interesting part of the article is the cometary on where basic research is going to happen in the future. The fear is that if it only happens in large companies, then the unbiased pool of experts society relies on will be smaller and less informed. Along with the issue of nobody being around for the slog of defining a field, setting up databases, competitions and standards. These are what allow well fu…
> DeepMind claimed that they used “128 TPUv3 cores or roughly equivalent to ~100-200 GPUs”. Although this amount of compute seems beyond the wildest dreams of most academic researchers... So, we're talking like what? Maybe $100K to $300K of hardware? Wet biology labs often have multiple pieces of $100K+ equipment at their disposal. Why shouldn't computational labs too?
Earlier quoted context omitted.
The structure is the whole point. As I understand it, you can link together nearly arbitrary sequences of amino acids. But a random string of AAs will just result in a jumbled protein that doesn't do anything useful. Specific structures are useful in all manner of ways, from cleaving a DNA molecule at a specific point, enzymes for breaking apart molecules, etc. Very, very useful.
>Very, very useful. Just to frame it a particular way, biological systems are basically solved nanotechnology, extremely good, self-sustaining, resilient little machines that have spent a long time optimizing to be better and better. But all the designs are preset, if we can crack the code and design our own little machines, then amazing things like more plastic-like cellulose could be made, all sorts of problems are…
Generally speaking molecular nanotechnology will solve all the "intractable" problems we as a society face today: climate change, poverty, biological death from old age / disease / cancer, and more.
We could also create tools of destruction so vast, it can be hard to contemplate.
What are the big implications of being good at predicting protein structures?
- better predict drug binding to proteins (massive benefits if accurate)
- better understand the functional outcomes of missense mutations on proteins
- study protein-protein interactions
- and in general, just gain a better understanding of biology (which is driven by proteins and their reactions/interactions)Earlier quoted context omitted.
Yeah but it's also putting it together and properly utlizing it, which takes specialist knowledge.
This was never really a bottleneck for science. One needs to realize first that something can be done, then it will be done. The cost of computing will also go down in the future and for government funds this sounds like a drop in the sea when they are building multi billion dollar particle accelerators.
I guess the main reason it hasn't been done is that the deprecation is still huge due to chip advancements.
What are the big implications of being good at predicting protein structures?
If we can accurately predict protein structures (particularly multiple structures, or structures reflecting what the conformation is in cells), then we can do a couple things: - better predict drug binding to proteins (massive benefits if accurate) - better understand the functional outcomes of missense mutations on proteins - study protein-protein interactions - and in general, just gain a better understanding of bi…
Earlier quoted context omitted.
This was never really a bottleneck for science. One needs to realize first that something can be done, then it will be done. The cost of computing will also go down in the future and for government funds this sounds like a drop in the sea when they are building multi billion dollar particle accelerators.
Imagine building a multi billion dollar computing cluster solely for research. I guess the main reason it hasn't been done is that the deprecation is still huge due to chip advancements.