Molecular free energies, rates, and mechanisms from data-efficient path sampling simulations

TL;DR

This paper introduces a data-efficient algorithm that combines machine learning with transition path sampling to accurately compute free energies, rates, and mechanisms of rare molecular events from unbiased trajectories.

Contribution

The authors developed a novel algorithm that reconstructs equilibrium path ensembles from machine learning-guided sampling, enabling efficient calculation of thermodynamic and kinetic properties.

Findings

Successfully applied to chignolin folding

Provides accurate free energies and rates

Requires moderate computational resources

Abstract

Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of complex molecular events. However, these simulations can rarely sample the required time scales in practice. Transition path sampling overcomes this limitation by collecting unbiased trajectories capturing the relevant events. Moreover, the integration of machine learning can boost the sampling while simultaneously learning a quantitative representation of the mechanism. Still, the resulting trajectories are by construction non-Boltzmann-distributed, preventing the calculation of free energies and rates. We developed an algorithm to approximate the equilibrium path ensemble from machine learning-guided path sampling data. At the same time, our algorithm provides efficient sampling, the mechanism, free energy, and rates of rare molecular events at a very moderate computational cost. We tested the method on the folding of the mini-protein chignolin. Our algorithm is straightforward and data-efficient, opening the door to applications on many challenging molecular systems.