提出基于框架的扩散模型，实现3D分子生成的精确对称性与高效采样。
Frame-based Equivariant Diffusion Models for 3D Molecular Generation
- 用全局/局部框架解耦对称性处理与主干网络，提升灵活性。
- 在QM9上达-141.85的测试负对数似然，分子稳定性90.51%。
- 适合需要物理合理性与快速生成的分子设计研究者。
现有分子生成方法在严格保持E(3)对称性与可扩展性之间存在权衡。本文提出基于框架的扩散范式，实现确定性E(3)-等变性，同时将对称性处理与主干网络解耦。基于此，提出三种变体：全局框架扩散（GFD）、局部框架扩散（LFD）和不变框架扩散（IFD）。为增强表达能力，引入边感知注意力的Diffusion Transformer（EdgeDiT）。在QM9数据集上，结合EdgeDiT的GFD在标准尺度下测试负对数似然达-137.97，双倍尺度下达-141.85，原子稳定性98.98%，分子稳定性90.51%，超越所有等变基线方法，且采样速度接近2倍于EDM。本研究确立了基于框架的扩散模型在分子生成中的可扩展、灵活且物理合理的范式，凸显全局结构保持的关键作用。
原文摘要 · Abstract (English)
Recent methods for molecular generation face a trade-off: they either enforce strict equivariance with costly architectures or relax it to gain scalability and flexibility. We propose a frame-based diffusion paradigm that achieves deterministic E(3)-equivariance while decoupling symmetry handling from the backbone. Building on this paradigm, we investigate three variants: Global Frame Diffusion (GFD), which assigns a shared molecular frame; Local Frame Diffusion (LFD), which constructs node-specific frames and benefits from additional alignment constraints; and Invariant Frame Diffusion (IFD), which relies on pre-canonicalized invariant representations. To enhance expressivity, we further utilize EdgeDiT, a Diffusion Transformer with edge-aware attention. On the QM9 dataset, GFD with EdgeDiT achieves state-of-the-art performance, with a test NLL of -137.97 at standard scale and -141.85 at double scale, alongside atom stability of 98.98%, and molecular stability of 90.51%. These results surpass all equivariant baselines while maintaining high validity and uniqueness and nearly 2x faster sampling compared to EDM. Altogether, our study establishes frame-based diffusion as a scalable, flexible, and physically grounded paradigm for molecular generation, highlighting the critical role of global structure preservation.
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