用多尺度训练加速分子生成，提升效率与泛化能力。
Multigrid Training for Molecular Generation using Graph Neural Networks
- 在不同分辨率图上分步训练，通过参数迁移加速学习。
- 3D分子生成任务中收敛速度提升，生成质量更优。
- 适合需要高效分子设计的药物研发人员使用。
深度学习在建模生化分子系统方面表现优异，输入通常以图或3D网格形式表示。但计算成本随分辨率升高而急剧增加，导致全分辨率计算昂贵且不稳定。本文提出一种多网格训练策略，利用低分辨率优化加速高分辨率学习，通过跨离散化传递参数实现。对于图结构分子表示，通过有偏随机游走上采样，将粗粒度图学到的参数逐步迁移到越来越精细的图中；对于3D分子生成，对分子结构在多分辨率下体素化，先在粗分辨率上预训练条件变分自编码器（CVAE），再将形状兼容的卷积参数从粗模型初始化细分辨率CVAE。在受体条件下的3D配体生成任务中，多网格训练相比从头训练显著加速收敛并提升泛化性能。
原文摘要 · Abstract (English)
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 generation, we voxelize the molecular structures at multiple resolutions, pretrain a coarse-resolution conditional Variational Autoencoder (CVAE), and initialize a fine-resolution CVAE by transferring shape compatible convolutional parameters from the coarse model. Numerical experiments on receptor-conditioned 3D Ligand generation show that multigrid training accelerates convergence and improves generalization compared to training from scratch.
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