More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations

TL;DR

This study compares four similarity measures for unsupervised pathway detection in molecular simulations, finding that simpler measures like Euclidean distance often perform as well as more complex ones, depending on the system.

Contribution

It evaluates and compares the effectiveness of various similarity measures in trajectory clustering for biomolecular simulations, highlighting when simplicity suffices.

Findings

Wasserstein distances perform best in a benchmark system.

Euclidean distances are computationally efficient and effective in complex systems.

More sophisticated measures are not always necessary for meaningful clustering.

Abstract

Finding process pathways in molecular simulations such as the unbinding paths of small molecule ligands from their binding sites at protein targets in a set of trajectories via unsupervised learning approaches requires the definition of a suitable similarity measure between trajectories. We here evaluate the performance of four such measures with varying degree of sophistication, i.e., Euclidean and Wasserstein distances, Procrustes analysis and dynamical time warping, when analyzing trajectory data from two different biased simulation driving protocols in the form of constant velocity constraint targeted MD and steered MD. In a streptavidin-biotin benchmark system with known ground truth clusters, Wasserstein distances yielded the best clustering performance, closely followed by Euclidean distances, both being the most computationally efficient similarity measures. In a more complex A2a receptor-inhibitor system, however, the simplest measure, i.e., Euclidean distances, was sufficient to reveal meaningful and interpretable clusters.