Learning Collective Variables with Synthetic Data Augmentation through Physics-inspired Geodesic Interpolation
arxiv(2024)
摘要
In molecular dynamics simulations, rare events, such as protein folding, are
typically studied using enhanced sampling techniques, most of which are based
on the definition of a collective variable (CV) along which acceleration
occurs. Obtaining an expressive CV is crucial, but often hindered by the lack
of information about the particular event, e.g., the transition from unfolded
to folded conformation. We propose a simulation-free data augmentation strategy
using physics-inspired metrics to generate geodesic interpolations resembling
protein folding transitions, thereby improving sampling efficiency without true
transition state samples. Leveraging interpolation progress parameters, we
introduce a regression-based learning scheme for CV models, which outperforms
classifier-based methods when transition state data are limited and noisy.
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