Multigrid Training for Molecular Generation using Graph Neural Networks
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. We introduce a multigrid training strategy that leverages low-resolution optimization to accelerate learning at higher resolution through parameter transfer across discretizations. For graph molecular representations, we progressively transfer parameters learned from a coarse graph to a sequence of increasingly finer graphs via biased random walk upsampling. For 3D molecular genera...
| Authors | Zixuan Ling, Paula Mercurio, Di Liu |
|---|---|
| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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