Molecular Latent Space Simulators

TL;DR

This paper introduces latent space simulators that leverage deep learning to generate continuous all-atom molecular trajectories efficiently, enabling ultra-long simulations with high accuracy and significantly reduced computational cost.

Contribution

The paper presents a novel deep learning framework that learns slow collective variables, propagates dynamics, and reconstructs molecular configurations for continuous all-atom simulations.

Findings

Successfully generated ultra-long folding trajectories for Trp-cage miniprotein.

Achieved six orders of magnitude reduction in computational cost compared to traditional MD.

Produced trajectories that accurately reproduce molecular structure, thermodynamics, and kinetics.

Abstract

Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.