title: Towards Unified Latent Space for 3D Molecular Latent Diffusion Modeling
summary: 3D molecule generation is crucial for drug discovery and material science,
  requiring models to process complex multi-modalities, including atom types,
  chemical bonds, and 3D coordinates. A key challenge is integrating these
  modalities of different shapes while maintaining SE(3) equivariance for 3D
  coordinates. To achieve this, existing approaches typically maintain separate
  latent spaces for invariant and equivariant modalities, reducing efficiency in
  both training and sampling. In this work, we propose Unified
  Variational Auto-Encoder for 3D Molecular Latent
  Diffusion Modeling (UAE-3D), a multi-modal VAE that compresses 3D
  molecules into latent sequences from a unified latent space, while maintaining
  near-zero reconstruction error. This unified latent space eliminates the
  complexities of handling multi-modality and equivariance when performing latent
  diffusion modeling. We demonstrate this by employing the Diffusion
  Transformer--a general-purpose diffusion model without any molecular inductive
  bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9
  datasets demonstrate that our method significantly establishes new benchmarks
  in both de novo and conditional 3D molecule generation, achieving
  leading efficiency and quality.
paper_authors._id: 67dcc734067589b43b19af7a
paper_authors.name: Yanchen Luo
paper_authors._id: 67dcc734067589b43b19af7b
paper_authors.name: Zhiyuan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:52:14.275Z
paper_authors._id: 6472f1f7485a7c8e1bcf507b
paper_authors.avatarUrl: /avatars/79dca137583875191ea883b193b630b7.svg
paper_authors.fullname: Zhiyuan Liu
paper_authors.type: user
paper_authors.user: zibuyu9
paper_authors._id: 67dcc734067589b43b19af7c
paper_authors.name: Yi Zhao
paper_authors._id: 67dcc734067589b43b19af7d
paper_authors.name: Sihang Li
paper_authors._id: 67dcc734067589b43b19af7e
paper_authors.name: Kenji Kawaguchi
paper_authors._id: 67dcc734067589b43b19af7f
paper_authors.name: Tat-Seng Chua
paper_authors._id: 67dcc734067589b43b19af80
paper_authors.name: Xiang Wang
paper_title: Towards Unified Latent Space for 3D Molecular Latent Diffusion Modeling
paper_summary: 3D molecule generation is crucial for drug discovery and material science,
  requiring models to process complex multi-modalities, including atom types,
  chemical bonds, and 3D coordinates. A key challenge is integrating these
  modalities of different shapes while maintaining SE(3) equivariance for 3D
  coordinates. To achieve this, existing approaches typically maintain separate
  latent spaces for invariant and equivariant modalities, reducing efficiency in
  both training and sampling. In this work, we propose Unified
  Variational Auto-Encoder for 3D Molecular Latent
  Diffusion Modeling (UAE-3D), a multi-modal VAE that compresses 3D
  molecules into latent sequences from a unified latent space, while maintaining
  near-zero reconstruction error. This unified latent space eliminates the
  complexities of handling multi-modality and equivariance when performing latent
  diffusion modeling. We demonstrate this by employing the Diffusion
  Transformer--a general-purpose diffusion model without any molecular inductive
  bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9
  datasets demonstrate that our method significantly establishes new benchmarks
  in both de novo and conditional 3D molecule generation, achieving
  leading efficiency and quality.
paper_ai_summary: A unified variational auto-encoder for 3D molecular latent diffusion modeling compresses multi-modal molecular data into a single latent space, improving efficiency and quality in 3D molecule generation.
paper_ai_keywords: SE(3) equivariance latent sequences unified latent space near-zero reconstruction error latent diffusion modeling Diffusion Transformer de novo molecule generation conditional molecule generation