Generative Coarse-Graining of Molecular Conformations
This work addresses the problem of information loss in molecular simulations for researchers in computational chemistry and biophysics, representing a novel method for a known bottleneck.
The paper tackles the challenge of accurately restoring fine-grained molecular coordinates from coarse-grained representations, a process known as backmapping, by proposing a generative model that encodes uncertainties into an invariant latent space and decodes them via equivariant convolutions. Experiments demonstrate that the approach recovers more realistic structures and outperforms existing data-driven methods by a significant margin.
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, such CG procedure induces information losses, which makes accurate backmapping, i.e., restoring fine-grained (FG) coordinates from CG coordinates, a long-standing challenge. Inspired by the recent progress in generative models and equivariant networks, we propose a novel model that rigorously embeds the vital probabilistic nature and geometric consistency requirements of the backmapping transformation. Our model encodes the FG uncertainties into an invariant latent space and decodes them back to FG geometries via equivariant convolutions. To standardize the evaluation of this domain, we provide three comprehensive benchmarks based on molecular dynamics trajectories. Experiments show that our approach always recovers more realistic structures and outperforms existing data-driven methods with a significant margin.