Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network

Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) trained on all-atom (AA) simulations can serve as CG force fields, which have demonstrated success in CG simulations of proteins. Herein, we built data sets of AA simulations of DOPC, DOPS, and mixed DOPC/DOPS lipid bilayers and developed the first GNN-based CG lipid models based on the TorchMD-GN architecture. The CG lipid models reproduce the structural correlations of the AA simulations, accelerate the lipid dynamics by 9.4 times, and exhibit some degree of temperature transferability. Moreover, we demonstrate that training CG models on lipid bicelles enhances the performance of models in the lipid self-assembly and vesicle simulations. Our findings indicate that GNN-based CG lipid force fields show promise as a powerful approach for large-scale membrane simulations.