Being able to quantify the importance of the environment is one advantage of using a simulator based approach. You know what's happening, and you can simulate other environments by adding the relevant molecules around.
Speeding-up the folding is not the real problem, knowing what happen is. One way to speed-up the process is just to minimize the free-energy of the configuration (or some other quantity you derive from the neural network vector potential). (That's what the game fold-it was about : minimizing the Rosetta energy function). An other way would be to just use generative method like diffusion model to generate a plausible full trajectory (but you need some training dataset to bootstrap the process). Or work with key-configuration frames. The simulation can take a long time but it goes through specific arrangements (the transitions between energy plateau), and you learn these key points.
The simulator can also be much faster because it doesn't have to consider all the pair of atom arrangements (n^2 behavior if you are naive) into O(n) with n the number of atoms (with the bigger constant which is running the neural network hidden inside the O notation).
The simulations are classical but fundamentally they rely on the shape of the electron clouds. The electron density can deform (that's what bonding is), providing additional degrees of liberty, allowing the atom configuration to slide more easily against itself and avoid getting stuck in local optimum. Fortunately all this mess is nicely encapsulated inside the neural network potential and we can work without worrying about the electrons, their shape being implicitly defined by the current position of the atoms (using the implicit function theorem make abstracting their behaviour sound because of the faster timescales).
Speeding-up the folding is not the real problem, knowing what happen is. One way to speed-up the process is just to minimize the free-energy of the configuration (or some other quantity you derive from the neural network vector potential). (That's what the game fold-it was about : minimizing the Rosetta energy function). An other way would be to just use generative method like diffusion model to generate a plausible full trajectory (but you need some training dataset to bootstrap the process). Or work with key-configuration frames. The simulation can take a long time but it goes through specific arrangements (the transitions between energy plateau), and you learn these key points.
The simulator can also be much faster because it doesn't have to consider all the pair of atom arrangements (n^2 behavior if you are naive) into O(n) with n the number of atoms (with the bigger constant which is running the neural network hidden inside the O notation).
The simulations are classical but fundamentally they rely on the shape of the electron clouds. The electron density can deform (that's what bonding is), providing additional degrees of liberty, allowing the atom configuration to slide more easily against itself and avoid getting stuck in local optimum. Fortunately all this mess is nicely encapsulated inside the neural network potential and we can work without worrying about the electrons, their shape being implicitly defined by the current position of the atoms (using the implicit function theorem make abstracting their behaviour sound because of the faster timescales).