3 ms·

That doesn't line up with what we're seeing. Especially around supply chain, conversations have included the chemistry/materials data teams at some of the bigge

That doesn't line up with what we're seeing. Especially around supply chain, conversations have included the chemistry/materials data teams at some of the biggest companies. Likewise, academic researchers have steadily been switching over from past methods. GNNs here feels very much like imagenet for NNs for perception. It's hard to call that not a big deal. Many aren't switching, but not because they disbelieve it. For example, in precision medicine, a Stanford professor was mostly skeptical of practical relevance because of the general gap between real-world data findings and the multi-year clinical trial process... but that's not about GNNs, but the well-known issue of institutional resistance to real-world data. (We're biased: We're working with enterprise to bring in GNNs more on the supply chain, finance, fraud, security, etc. sides.)

COGlory 5y ago
I too am using AlphaFold when I can, and it is immensely valuable as a tool. The same way Phyre and ITASSER were immensely valuable. But it still suffers from the same categorical limitations that all homology modeling suffers from (although those limitations aren't reaching the public narrative, likely because they're technical details). It's a generational improvement in homology modeling, which is nice, but not particularly groundbreaking, and doesn't get us a whole lot closer to solving the big problems of drug design. It's still lousy when there's inadequate folding, it's lousy at oligomeric state (although that can probably be improved), and it's got tons of baked in, innate bias. It's also lousy at conformational states (but so is most empirical data). It's good enough for something that doesn't need to be high resolution, but so were preexisting methods. It's not good enough to predict a fold de novo.
- lmeyerov 5y ago
  Most scientists fail to change a field so quickly like this, and so greatly improve the quality of results, and I've worked with Turing award winners who can't say the same. It's hard not to call that ground breaking. Ex: https://www.icr.ac.uk/blogs/the-drug-discoverer/page-details/reflecting-on-deepmind-s-alphafold-artificial-intelligence-success-what-s-the-real-significance-for-protein-folding-research-and-drug-discovery https://www.icr.ac.uk/blogs/the-drug-discoverer/page-details... """ Not surprisingly Lupas was impressed that AlphaFold enabled him to determine in half an hour the structure of a protein that he’d failed to solve for 10 years! """ From a computational / biology perspective, this is also important as part of the broader push to get end-to-end simulation & neural networks for areas beyond individual protein structures. Imagenet is a really good analogy: what started with a jump in mnist tasks is now at the level of deepfakes and multi-modal audiovisual inference. The leaders of the field defined the bar for what it means to do well in this area, and with alphafold doing well, better and composite solutions can now happen. (And more subtlely, they showed ~GNNs work in this area in general, which was not viable till recently.)
greazy 5y ago
From academia, it depends on which field. Too much of a black box to rely on nns
- lmeyerov 5y ago
  Yes, hence my example of clinical trialists. Observationally... when NN's enter a field like this, they seem to grow in use, not shrink. There's an uncomfortable epistemological, and arguably Darwinian, counterpoint to rejecting blackboxes or mathematically unprincipled methods, particularly when their objective results are superior. So for many people who claim NN's can't be used, that's often from a subjective methodology preference, and modern history seems to be on the side of NNs. I dislike that's true of a leading method, but that's where a lot of modern modeling is.

COGlory 5y ago

I too am using AlphaFold when I can, and it is immensely valuable as a tool. The same way Phyre and ITASSER were immensely valuable. But it still suffers from the same categorical limitations that all homology modeling suffers from (although those limitations aren't reaching the public narrative, likely because they're technical details). It's a generational improvement in homology modeling, which is nice, but not particularly groundbreaking, and doesn't get us a whole lot closer to solving the big problems of drug design. It's still lousy when there's inadequate folding, it's lousy at oligomeric state (although that can probably be improved), and it's got tons of baked in, innate bias. It's also lousy at conformational states (but so is most empirical data). It's good enough for something that doesn't need to be high resolution, but so were preexisting methods. It's not good enough to predict a fold de novo.

lmeyerov 5y ago
Most scientists fail to change a field so quickly like this, and so greatly improve the quality of results, and I've worked with Turing award winners who can't say the same. It's hard not to call that ground breaking. Ex: https://www.icr.ac.uk/blogs/the-drug-discoverer/page-details/reflecting-on-deepmind-s-alphafold-artificial-intelligence-success-what-s-the-real-significance-for-protein-folding-research-and-drug-discovery https://www.icr.ac.uk/blogs/the-drug-discoverer/page-details... """ Not surprisingly Lupas was impressed that AlphaFold enabled him to determine in half an hour the structure of a protein that he’d failed to solve for 10 years! """ From a computational / biology perspective, this is also important as part of the broader push to get end-to-end simulation & neural networks for areas beyond individual protein structures. Imagenet is a really good analogy: what started with a jump in mnist tasks is now at the level of deepfakes and multi-modal audiovisual inference. The leaders of the field defined the bar for what it means to do well in this area, and with alphafold doing well, better and composite solutions can now happen. (And more subtlely, they showed ~GNNs work in this area in general, which was not viable till recently.)

greazy 5y ago

From academia, it depends on which field. Too much of a black box to rely on nns

lmeyerov 5y ago
Yes, hence my example of clinical trialists. Observationally... when NN's enter a field like this, they seem to grow in use, not shrink. There's an uncomfortable epistemological, and arguably Darwinian, counterpoint to rejecting blackboxes or mathematically unprincipled methods, particularly when their objective results are superior. So for many people who claim NN's can't be used, that's often from a subjective methodology preference, and modern history seems to be on the side of NNs. I dislike that's true of a leading method, but that's where a lot of modern modeling is.