3 ms·

Both projects tackle related problems, but each is trying to answer a different question: https://news.ycombinator.com/item?id=32264059 https://news.ycombinator

• westurner 4y ago
  > Folding@home answers a related but different question. While AlphaFold returns the picture of a folded protein in its most energetically stable conformation, Folding@home returns a video of the protein undergoing folding, traversing its energy landscape. Is there any NN architectural reason that AlphaFold could not learn and predict the Folding@home protein folding interactions as well? Is there yet an open implementation?
  • dekhn 4y ago
    I think it would be much harder to do that, since it probably requires modelling physics at some level, while AlphaFold is really just mining statistical correlations of structures and sequences. Yes, there are open implementations of nearly-AlphaFold at this point.
    • westurner 4y ago
      FWIU there's no algorithmic reason that AlphaZero-style self play w/ rules could not learn the quantum chemistry / physics. Given the infinite monkey theorem, can an e.g. bayesian NN learn quantum gravity enough to predictively model multibody planetary orbits given an additional solar mass in transit through the solar system? (What about with "try Bernoulli's on GR and call it superfluid quantum gravity" or "the bond yield-curve inversion is a known-good predictor, with lag" as Goal-programming nudges to distributedly-partitioned symbolic EA/GA with a cost/error/survival/fitness function?) E.g. re-derivations of Lean Mathlib would be the strings to evolve.