Understanding Reaction Mechanisms from Start to Finish

TL;DR

This paper presents an iterative neural network-based method to accurately compute the committor function, enabling detailed mechanistic insights into complex molecular transition processes.

Contribution

It introduces a novel iterative path sampling strategy that combines reweighting and neural network training to efficiently determine the committor in high-dimensional systems.

Findings

Successfully applied to a 2D potential benchmark.

Effectively characterized a complex host-guest binding process.

Converged to a reliable reaction coordinate for mechanistic analysis.

Abstract

Understanding mechanisms of rare but important events in complex molecular systems, such as protein folding or ligand (un)binding, requires accurately mapping transition paths from an initial to a final state. The committor is the ideal reaction coordinate for this purpose, but calculating it for high-dimensional, nonlinear systems has long been considered intractable. Here, we introduce an iterative path sampling strategy for computing the committor function for systems with high free energy barriers. We start with an initial guess to define isocommittor interfaces for transition interface sampling. The resulting path ensemble is then reweighted and used to train a neural network, yielding a more accurate committor model. This process is repeated until convergence, effectively solving the long-standing circular problem in enhanced sampling where a good reaction coordinate is needed to generate efficient sampling, and vice-versa. The final, converged committor model can be interrogated to extract mechanistic insights. We demonstrate the power of our method on a benchmark 2D potential and a more complex host-guest (un)binding process in explicit solvent.