3 ms·
AlphaFold doesn’t work for engineering though. Getting a shitty answer ends up being worse than useless. It seems to really accelerate productivity of research
by adastra22 2y ago
AlphaFold doesn’t work for engineering though. Getting a shitty answer ends up being worse than useless.
It seems to really accelerate productivity of researchers investigating bio molecules or molecules very similar to existing bio molecules. But not de novo stuff.
- roughly 2y agoEh, in many cases for actual customer-facing commercial work, they're sticking remarkably close to stuff that's in genbank/swissprot/etc - well characterized molecules and pathways, because working with genuinely de novo stuff is difficult and expensive. In those cases, Alphafold works fine - it always requires someone to actually look at the results and see whether they make sense or not, but also "the part of the solution space where the tools work" is often a deciding factor in what approach is chosen.
- make3 2y agothat's just not true. In a lot of cases in engineering, there are 10000000 possibilities, and deeplearning shows you 100 potentially more promising ones to double check, and that's worth huge amounts of money. In a lot of cases deep learning is able to simulate complex system at a precision that is more than precise enough, that otherwise would not be tracktable (like is the case with alphafold), and again this is especially valuable if you can double check the output. Ofc, in the field of language and vision and in a lot of other fields, deep learning is straight up the only solution.