  Biological functions of RNAs are determined by their three-dimensional (3D)
structures. Thus, given the limited number of experimentally determined RNA
structures, the prediction of RNA structures will facilitate elucidating RNA
functions and RNA-targeted drug discovery, but remains a challenging task. In
this work, we propose a Graph Neural Network (GNN)-based scoring function
trained only with the atomic types and coordinates on limited solved RNA 3D
structures for distinguishing accurate structural models. The proposed
Physics-aware Multiplex Graph Neural Network (PaxNet) separately models the
local and non-local interactions inspired by molecular mechanics. Furthermore,
PaxNet contains an attention-based fusion module that learns the individual
contribution of each interaction type for the final prediction. We rigorously
evaluate the performance of PaxNet on two benchmarks and compare it with
several state-of-the-art baselines. The results show that PaxNet significantly
outperforms all the baselines overall, and demonstrate the potential of PaxNet
for improving the 3D structure modeling of RNA and other macromolecules. Our
code is available at https://github.com/zetayue/Physics-aware-Multiplex-GNN.
