MolCluster: An Unsupervised Framework for Multiscale Molecular Representations with Physically Consistent Resolution Control

Coarse-grained (CG) modeling simplifies molecular systems
by mapping
groups of atoms into representative particles, but traditional approaches
depend on fixed rules and struggle to handle diverse chemical structures.
Supervised CG methods further suffer from limited labeled datasets
and the inability to control mapping resolution, which is essential
for multiscale modeling. To overcome these limitations, we propose
MolCluster, an unsupervised model that integrates graph neural networks
and community detection algorithm to extract CG representations. Additionally,
a predefined group pair loss ensures the preservation of target groups,
and a bisection strategy enables precise, customizable resolution
across different molecular systems. In the case of downstream task,
evaluations on the MARTINI2 dataset demonstrate that MolCluster, benefiting
from its label-free pretraining strategy, outperforms both traditional
clustering and supervised models in CG mapping and bead type prediction.
Overall, these results highlight the potential of MolCluster as a
base model for customizable and chemically consistent CG mapping,
with future applications extending to polymers, proteins, and other
complex multiscale systems.