Frame-based Equivariant Diffusion Models for 3D Molecular Generation

• URL: http://arxiv.org/abs/2509.19506v2
• Date: Mon, 06 Oct 2025 11:26:19 GMT
• Title: Frame-based Equivariant Diffusion Models for 3D Molecular Generation
• Authors: Mohan Guo, Cong Liu, Patrick Forré,
• Abstract summary: We study frame-based diffusion as a scalable, flexible, and physically grounded paradigm for molecular generation.<n>Our study establishes frame-based diffusion as a scalable, flexible, and physically grounded paradigm for molecular generation.
• Score: 16.663144492330247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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