For an unrelated reason, shortly after making that comment, I put 31 genes from a viral genome (the whole genome, assuming we have the reading frames correct and nothing else funky is going on) through AlphaFold. We're getting ready to do some proteomics to see what's in the capsid, and I wanted to inform the proteomics by doing some sequence analysis. Only three genes of the 31 came back with any sort of confidence. Two of the three were crystallized and solved by my group a few years back.
FWIU, Folding@home has additional problems for AlphaFold, if not the AlphaFold team;
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> which started at Washington University in St. Louis, home of the Human Genome Project
While Wash U was a contributor, I am confused about why you call it the home of the Human Genome Project. The Project seems a lot more strongly linked to the Whitehead/MIT in terms of press and the site of key figures.
Together, these teams have achieved a very significant cost reduction: the link I shared cites a sub-$1K cost to sequence a genome today; a cost savings of millions of dollars per genome.
> 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?
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.
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.
Pretty much everything you said doesn't make any sense. Folding@Home started at Stanford, not WashU. WashU was also not "the home of the human genome project", that was a distributed effort. AlphaFold doesn't contribute to Folding@Home, it's an entirely different problem.
