ICML 2025

Boosting Protein Graph Representations through Static-Dynamic Fusion
Pengkang Guo 1 Bruno Correia 1 Pierre Vandergheynst 1 Daniel Probst 1 2
Abstract
Machine learning for protein modeling faces sig-
nificant challenges due to proteins’ inherently
dynamic nature, yet most graph-based machine
learning methods rely solely on static structural
information. Recently, the growing availability of
molecular dynamics trajectories provides new op-
portunities for understanding the dynamic behav-
ior of proteins; however, computational methods
for utilizing this dynamic information remain lim-
ited. We propose a novel graph representation that
integrates both static structural information and
dynamic correlations from molecular dynamics
trajectories, enabling more comprehensive model-
ing of proteins. By applying relational graph neu-
ral networks (RGNNs) to process this heteroge-
neous representation, we demonstrate significant
improvements over structure-based approaches
across three distinct tasks: atomic adaptability
prediction, binding site detection, and binding
affinity prediction. Our results validate that com-
bining static and dynamic information provides
complementary signals for understanding protein-
ligand interactions, offering new possibilities for
drug design and structural biology applications.
1. Introduction
With the recent surge and successes of deep learning meth-
ods in protein structure prediction, attention is rapidly turn-
ing towards the prediction of the temporal behavior of these
highly dynamic macromolecules. Combined with quantita-
tive and qualitative advances in molecular dynamics simu-
lations (Joshi & Deshmukh, 2021; Zeng et al., 2021; Ma-
jewski et al., 2023; Nam & Wolf-Watz, 2023), this attention
is resulting in the increased availability and accessibility of
simulated molecular dynamics trajectories (Vander Meer-
1 ´Ecole Polytechnique F´ed´erale de Lausanne, Lausanne,
Switzerland
2Wageningen University & research, Wagenin-
gen, The Nether...