Disclaimer: I'm a professional (computational) structural biologist. My opinion is slightly different than the researcher that commented on the linked post.
I didn't see any claim by DeepMind that protein structure prediction is a solved problem. I think these guys are pretty diligent when it comes to communicating their science. What you may have seen, is a non-scientist reporter making inaccurate claims.
The problem with the structure prediction problem is not a loss/energy function problem, even if we had an accurate model of all the forces involved we'd still not have an accurate protein structure prediction algorithm.
Protein folding is a chaotic process (similar to the 3 body problem). There's an enormous number of interactions involved - between different amino acids, solvent and more. Numerical computation can't solve chaotic systems because floating point numbers have a finite representation, which leads to rounding errors and loss of accuracy.
Besides, Short range electro static and van der waals interactions are pretty well understood and before alphafold many algorithms (like Rosetta) were pretty successful in a lot of protein modeling tasks.
Therefore, we need a *practical* way to look at protein structure determination that is akin to AlphaFold2.
I didn't see any claim by DeepMind that protein structure prediction is a solved problem. I think these guys are pretty diligent when it comes to communicating their science. What you may have seen, is a non-scientist reporter making inaccurate claims.
The problem with the structure prediction problem is not a loss/energy function problem, even if we had an accurate model of all the forces involved we'd still not have an accurate protein structure prediction algorithm.
Protein folding is a chaotic process (similar to the 3 body problem). There's an enormous number of interactions involved - between different amino acids, solvent and more. Numerical computation can't solve chaotic systems because floating point numbers have a finite representation, which leads to rounding errors and loss of accuracy.
Besides, Short range electro static and van der waals interactions are pretty well understood and before alphafold many algorithms (like Rosetta) were pretty successful in a lot of protein modeling tasks.
Therefore, we need a *practical* way to look at protein structure determination that is akin to AlphaFold2.