The repetitive local sampling and the local distribution theory

Abstract Molecular simulation is a mature and versatile tool set widely utilized in many subjects. However, its methodology development has been struggling with a tradeoff between accuracy and speed, significant improvement of both is necessary to reliably substitute many expensive and laborious experiments in molecular biology and nanotechnology. Previously, the ubiquitous severe wasting of computational resources in molecular simulations due to repetitive local sampling was raised, and the local free energy landscape approach was proposed to address it. The core idea is to first learn local distributions, and followed by dynamic assembly of which to infer global joint distribution of a target molecular system. When compared with conventional explicit solvent molecular dynamics simulations, a simple and approximate implementation of this theory in protein structural refinement harvested acceleration of about six orders of magnitude without loss of accuracy. While this initial test revealed tremendous benefits for addressing repetitive local sampling, there are some implicit assumptions need to be articulated. Here, I present a more thorough discussion of repetitive local sampling; potential options for learning local distributions; a more general formulation with potential extension to simulation of near equilibrium molecular systems; the prospect of developing computation driven molecular science; the connection to mainstream residue pair distance distribution based protein structure studies; and the fundamental difference of the averaging strategy from potential of mean force. This more general development is termed the local distribution theory to release the limitation of strict thermodynamic equilibrium in its potential wide application in general soft condensed molecular systems. This article is categorized under: Molecular and Statistical Mechanics > Free Energy Methods Data Science > Artificial Intelligence/Machine Learning