I'm afraid not.

Actually, as someone with background in Biology I can tell you that this is not a problem you want to approach atoms-up. It's been tried, and our computational capabilities fell woefully short of succeeding.

I should explain what "woefully short" means, so that the answer won't be "but can't the AI apply more computational power than us?". Yes, presumably it can. But the scales are immense. To explain it, I will need an analogy.

Not that long ago, I had the notion that chess could be fully solved; that is, that you could simply describe every legal position and every position possible to reach from it, without duplicates, so you could use that decision tree to play a perfect game. After all, I reasoned, it's been done with checkers; surely it's just a matter of getting our computational power just a little bit better, right?

First I found a clever way to minimize the amount of bits necessary to describe a board position. I think I hit 34 bytes per position or so, and I guess further optimization was possible. Then, I set out to calculate how many legal board positions there are.

I stopped trying to be accurate about it when it turned out that the answer was in the vicinity of 10^68, give or take a couple orders of magnitude. That's about a billionth billionth of the TOTAL NUMBER OF ATOMS IN THE ENTIRE UNIVERSE. You would literally need more than our entire galaxy made into a huge database just to store the information, not to mention accessing it and computing on it.

So, not anytime soon.

Now, the problem with protein folding is, it's even more complex than chess. At the atomic level, it's incredibly more complex than chess. Our luck is, you don't need to fully solve it; just like today's computers can beat human chess players without spanning the whole planet. But they do it with heuristics, approximations, sometimes machine learning (though that just gives them more heuristics and approximations). We may one day be able to fold proteins, but we will do so by making assumptions and approximations, generating useful rules of thumb, not by modeling each atom.