  Biased enhanced sampling methods utilizing collective variables (CVs) are
powerful tools for sampling conformational ensembles. Due to high intrinsic
dimensions, efficiently generating conformational ensembles for complex systems
requires enhanced sampling on high-dimensional free energy surfaces. While
methods like temperature-accelerated molecular dynamics (TAMD) can adopt many
CVs in a simulation, unbiasing the simulation requires accurate modeling of a
high-dimensional CV probability distribution, which is challenging for
traditional density estimation techniques. Here we propose an unbiasing method
based on the score-based diffusion model, a deep generative learning method
that excels in density estimation across complex data landscapes. We test the
score-based diffusion unbiasing method on TAMD simulations. The results
demonstrate that this unbiasing approach significantly outperforms traditional
unbiasing methods, and can generate accurate unbiased conformational ensembles
for simulations with a number of CVs higher than usual ranges.
