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 the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.
Highlights
Molecular simulation has been successful in making predictions and understanding experimental data [1, 2]
Assessing the method on proteins not used for training distinguishes this approach from many approaches used to date for machine learning of molecular simulations
The purpose of learning a coarse-grained force field for proteins is to demonstrate that differentiable molecular simulation (DMS) can be used to learn all the parameters from scratch in simple, interpretable force fields
Summary
Molecular simulation has been successful in making predictions and understanding experimental data [1, 2]. It is generally agreed that force fields are not optimally parameterised [5, 6], for example significant effort has gone into modifying a handful of parameters in standard force fields to better represent both ordered and disordered proteins [7,8,9]. Such efforts have improved the force fields without changing their functional form, an attractive proposition when the alternative is adding complexity that restricts the timescales available for study. A number of groups have applied these advances to molecular simulations [11,12,13] including learning coarsegrained potentials [14,15,16,17,18], learning quantum mechanical potentials [19,20,21,22], improving
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