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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 t

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.)

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.)