  Studying the pathways of ligand-receptor binding is essential to understand
the mechanism of target recognition by small molecules. The binding free energy
and kinetics of protein-ligand complexes can be computed using molecular
dynamics (MD) simulations, often in quantitative agreement with experiments.
However, only a qualitative picture of the ligand binding/unbinding paths can
be obtained through a conventional analysis of the MD trajectories. Besides,
the higher degree of manual effort involved in analyzing pathways limits its
applicability in large-scale drug discovery. Here we address this limitation by
introducing an automated approach for analyzing molecular transition paths with
a particular focus on protein-ligand dissociation. Our method is based on the
dynamic time-warping (DTW) algorithm, originally designed for speech
recognition. We accurately classified molecular trajectories using a very
generic descriptor set of contacts or distances. Our approach outperforms
manual classification by distinguishing between parallel dissociation channels,
within the pathways identified by visual inspection. Most notably, we could
compute exit-path-specific ligand-dissociation kinetics. The unbinding
timescale along the fastest path agrees with the experimental residence time,
providing a physical interpretation to our entirely data-driven protocol. In
combination with appropriate enhanced sampling algorithms, this technique can
be used for the initial exploration of ligand-dissociation pathways as well as
for calculating path-specific thermodynamic and kinetic properties.
