Towards Unified Latent Space for 3D Molecular Latent Diffusion Modeling
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.
67dcc734067589b43b19af7a
Yanchen Luo
67dcc734067589b43b19af7b
Zhiyuan Liu
claimed_verified
2025-06-02T07:52:14.275Z
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/avatars/79dca137583875191ea883b193b630b7.svg
Zhiyuan Liu
user
zibuyu9
67dcc734067589b43b19af7c
Yi Zhao
67dcc734067589b43b19af7d
Sihang Li
67dcc734067589b43b19af7e
Kenji Kawaguchi
67dcc734067589b43b19af7f
Tat-Seng Chua
67dcc734067589b43b19af80
Xiang Wang
Towards Unified Latent Space for 3D Molecular Latent Diffusion Modeling
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.
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.
SE(3) equivariance latent sequences unified latent space near-zero reconstruction error latent diffusion modeling Diffusion Transformer de novo molecule generation conditional molecule generation