Geometry-complete latent diffusion model for 3D molecule generation.

Abstract

Generative models, especially diffusion models, have recently made remarkable progress in fields such as graph generation and drug design. However, current diffusion-based 3D molecule generation models still struggle with adequately modeling the true data distribution. We designed the geometry-complete latent diffusion model (GCLDM) to enhance the modeling capacity of diffusion models. A geometry-complete autoencoder for feature mapping between atom space and latent space is introduced. In addition, the latent space diffusion model can model continuous latent representations, which is helpful in learning to fit multi-modal feature distributions for the diffusion model. The comparative experimental results demonstrate that GCLDM could fit the true distribution of molecules well and outperform other state-of-art methods. Our codes and data are all provided at: [https://github.com/charlotte0104/GCLDM-for-3d-molucule-generation], and [https://zenodo.org/records/15773195].