Iterative variational learning of committor-consistent transition pathways using artificial neural networks

TL;DR

This paper presents a neural network method that iteratively learns transition pathways aligned with the committor function, improving the understanding of rare biomolecular events and transition states in high-dimensional systems.

Contribution

The paper introduces PCCANN, a neural network that refines transition pathways by aligning with the committor gradient, addressing sampling challenges in complex biomolecular simulations.

Findings

Successfully reproduces known dynamics and rate constants in benchmark systems

Reveals bifurcations and alternative pathways in biomolecular processes

Provides accurate estimates of transition states and free-energy barriers

Abstract

This contribution introduces a neural-network-based approach to discover meaningful transition pathways underlying complex biomolecular transformations in coherence with the committor function. The proposed path-committor-consistent artificial neural network (PCCANN) iteratively refines the transition pathway by aligning it to the gradient of the committor. This method addresses the challenges of sampling in molecular dynamics simulations rare events in high-dimensional spaces, which is often limited computationally. Applied to various benchmark potentials and biological processes such as peptide isomerization and protein-model folding, PCCANN successfully reproduces established dynamics and rate constants, while revealing bifurcations and alternate pathways. By enabling precise estimation of transition states and free-energy barriers, this approach provides a robust framework for enhanced-sampling simulations of rare events in complex biomolecular systems.