Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

ABSTRACT: Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce t

SUBMITTER: Greener JG 

PROVIDER: S-EPMC8412298 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature