Quantitative Pathway-Resolved Kinetics from Neural Network-Guided Weighted Ensemble Simulations

Many crucial biomolecular processes, such as ligand dissociation and conformational switching, occur via multiple competing transition pathways. While standard rare-event sampling methodologies provide rigorous estimates of the global transition rate, they typically collapse the underlying mechanistic heterogeneity into a single global constant, obscuring the relative kinetic contributions of distinct channels. Here, we introduce a path-resolved weighted ensemble (WE) framework that quantitatively assigns steady-state flux to competing transition channels without modifying the underlying unbiased dynamics. In our approach, initial reactive trajectories generated via direction-guided adaptive sampling are clustered by dynamic time warping (DTW) and smoothed into neural-network-refined representative paths. These refined routes are formulated as path collective variables (PathCVs) to preseed and guide channel-specific WE simulations. We demonstrate the robustness of this framework across systems of increasing complexity. On a model three-hole potential, the method accurately captures a temperature-dependent pathway crossover. In the benzamidine-trypsin complex, it isolates the dominant unbinding route while quantifying the fluxes of subdominant channels. Finally, for imatinib dissociation from Abl kinase, the method reveals that the clinically relevant N368S resistance mutation drastically redistributes reactive flux from the P-loop/hinge channel toward the αC-helix channel, accelerating escape. This framework successfully translates qualitative pathway classification into rigorous, pathway-specific kinetic observables, offering a powerful tool for understanding mutation-induced mechanistic shifts in biomolecular function.