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

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.