Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling
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| Format: | Preprint |
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2025
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| author | Luo, Yanchen Liu, Zhiyuan Zhao, Yi Li, Sihang Cai, Hengxing Kawaguchi, Kenji Chua, Tat-Seng Zhang, Yang Wang, Xiang |
| author_facet | Luo, Yanchen Liu, Zhiyuan Zhao, Yi Li, Sihang Cai, Hengxing Kawaguchi, Kenji Chua, Tat-Seng Zhang, Yang Wang, Xiang |
| contents | 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 \textbf{U}nified Variational \textbf{A}uto-\textbf{E}ncoder for \textbf{3D} Molecular Latent Diffusion Modeling (\textbf{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 \textit{de novo} and conditional 3D molecule generation, achieving leading efficiency and quality. On GEOM-Drugs, it reduces FCD by 72.6\% over the previous best result, while achieving over 70\% relative average improvements in geometric fidelity. Our code is released at https://github.com/lyc0930/UAE-3D/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling Luo, Yanchen Liu, Zhiyuan Zhao, Yi Li, Sihang Cai, Hengxing Kawaguchi, Kenji Chua, Tat-Seng Zhang, Yang Wang, Xiang Machine Learning 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 \textbf{U}nified Variational \textbf{A}uto-\textbf{E}ncoder for \textbf{3D} Molecular Latent Diffusion Modeling (\textbf{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 \textit{de novo} and conditional 3D molecule generation, achieving leading efficiency and quality. On GEOM-Drugs, it reduces FCD by 72.6\% over the previous best result, while achieving over 70\% relative average improvements in geometric fidelity. Our code is released at https://github.com/lyc0930/UAE-3D/. |
| title | Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.15567 |