"It's pattern matching, plain and simple, an area where pattern matching algorithms excel. Pattern matching driven decomp absolutely leads"
Decompilation is a dependence graph problem, one can formulate decompilation as a graph transformation/rewrite. Neural networks are notoriously bad at graphs.
AlphaFold is based on graph neural networks. The biggest issue is that we still do not know how to best encode graph problems in ways neural networks can exploit. Current graph neural network techniques exploit certain invariants but cannot distinguish between various similar graphs. And yet, they're still generating meaningful insights.
And what is the size of graphs processed by AlphaFold? How does it compare to the size of program dependence graph?
Here's evaluation of boolean satisfiability encoding of protein folding problem: https://pmc.ncbi.nlm.nih.gov/articles/PMC7197060/
Boolean satisfiability solvers are used in satisfiability modulo theories solvers that, in turn, are used to prove various things about programs.