Not if you're going off of ab initio theory such as Hartee Fock, MP2, CC, etc. We're talking amounts of matrix multiplication that wouldn't be enough to finish calculating this decade, even if you had parallel access to all top 500 supercomputers, you get bigger than a single protein, it's beyond universal time scales with current implementations.
Every time some computer scientist interviews me and shows off their O(n) knowledge (it's always an o(n) solution to a naive o(n**2) problem!) I mention that in the Real World, engineers routinely do O(n**7) calculations (n==number of basis functions) on tiny systems (up to about 50 atoms, maybe 100 now?) and if they'd like to help it would be nice to have better, faster approximations that are n**2 or better. Unfrotunately, the process of going from computer scientist to expert in QM is entirely nontrivial so most of them do ads ML instead
How on Earth? I can't imagine the difference between the computational power of all top 500 supercomputers is THAT many orders of magnitude far off from the computational power of all the folding@home computational power donated by the general public.
supercomputers are specialized products with fast networking to enable real-time updates between nodes. The total node count is limited by the cost of the interconnect to get near-peak performance. You typically run one very large simulation for a long period of simulation time. folding@home doesn't have the luxury of fast networks, jut lots of CPU/GPU. They run many smaller simulations for shorter times, then collect the results and do stats on them.
I looked at the various approaches and sided with folding@home. At one point I had 1 million fast CPU cores running gromacs.
