MemBack: An Equivariant Graph Neural Network for Backmapping Lipid Membranes

Abstract

Backmapping coarse-grained simulations to atomistic resolution is central to multiscale molecular simulation but remains challenging for chemically complex lipid membranes. We introduce MemBack, an SE(3) equivariant graph neural network that reconstructs CHARMM36 lipid structures from Martini 3 configurations by single-pass heavy-atom prediction followed by automated post-processing. Across chemically diverse systems, MemBack achieved a mean superposition-free per-lipid heavy-atom RMSD of 0.65 [A], while retaining comparable accuracy for membrane systems and lipid species excluded from training. At the membrane scale, MemBack preserved structural organization across resolutions: in a 16-component red-blood-cell membrane model excluded from training, bilayer thickness, area per lipid, and acyl-chain order closely matched the atomistic reference after only restrained minimization; in a native phase-separated Martini 3 membrane, the lateral organization of ordered and disordered domains was likewise retained directly after backmapping, without atomistic equilibration. Native Martini 3 systems containing up to 1.4 million reconstructed atoms remained consistent with their parent coarse-grained configurations and could be propagated in atomistic simulations, providing an efficient interface between Martini 3 and CHARMM36 membrane simulations.