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

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.)