Multigrid Training for Molecular Generation using Graph Neural Networks
Zixuan Ling, Paula Mercurio, D Liu
Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D grids. A major challenge is that computational cost scales with resolution, making full graph/grid computation of molecular densities expensive and often unstable. …