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Arxiv
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Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling
- 3D molecule generation is vital for drug discovery and material science, requiring models to handle complex multi-modalities.
- An important challenge is integrating modalities like atom types, chemical bonds, and 3D coordinates while maintaining SE(3) equivariance for 3D coordinates.
- Existing methods often use separate latent spaces for different modalities, affecting training and sampling efficiency.
- A Unified Variational Auto-Encoder for 3D Molecular Latent Diffusion Modeling (UAE-3D) is proposed to address this challenge.
- UAE-3D compresses 3D molecules into a unified latent space with near-zero reconstruction error, simplifying handling of multi-modalities.
- The unified latent space enables efficient latent diffusion modeling without the complexities of multi-modality handling.
- The Diffusion Transformer, a molecular-inductive-bias-free diffusion model, is used for latent generation.
- Extensive experiments on GEOM-Drugs and QM9 datasets show that UAE-3D sets new benchmarks in de novo and conditional 3D molecule generation.
- On GEOM-Drugs, FCD reduction by 72.6% compared to the previous best result is achieved, with over 70% relative average improvements in geometric fidelity.
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