Constructing Generalized Sample Transition Probabilities with Biased Simulations

TL;DR

This paper introduces GSTP, a novel method to accurately estimate unbiased transition probabilities from biased molecular dynamics simulations, enabling better kinetic analysis of complex systems.

Contribution

GSTP is a new approach that recovers unbiased transition probabilities without assuming a specific stochastic process or kernel form, improving kinetic analysis from biased data.

Findings

GSTP accurately recovers unbiased eigenvalues and eigenstates.

Validated on model systems including harmonic oscillator and peptides.

Effective in analyzing kinetics from biased simulations.

Abstract

In molecular dynamics (MD) simulations, accessing transition probabilities between states is crucial for understanding kinetic information, such as reaction paths and rates. However, standard MD simulations are hindered by the capacity to visit the states of interest, prompting the use of enhanced sampling to accelerate the process. Unfortunately, biased simulations alter the inherent probability distributions, making kinetic computations using techniques such as diffusion maps challenging. Here, we use a coarse-grained Markov chain to estimate the intrinsic pairwise transition probabilities between states sampled from a biased distribution. Our method, which we call the generalized sample transition probability (GSTP), can recover transition probabilities without relying on an underlying stochastic process and specifying the form of the kernel function, which is necessary for the diffusion map method. The proposed algorithm is validated on model systems such as a harmonic oscillator, alanine dipeptide in vacuum, and met-enkephalin in solvent. The results demonstrate that GSTP effectively recovers the unbiased eigenvalues and eigenstates from biased data. GSTP provides a general framework for analyzing kinetic information in complex systems, where biased simulations are necessary to access longer timescales.