邁向統一潛在空間的三維分子潛在擴散建模

Towards Unified Latent Space for 3D Molecular Latent Diffusion Modeling

March 19, 2025

作者: Yanchen Luo, Zhiyuan Liu, Yi Zhao, Sihang Li, Kenji Kawaguchi, Tat-Seng Chua, Xiang Wang

cs.AI

摘要

三維分子生成對於藥物發現和材料科學至關重要，這要求模型能夠處理複雜的多模態數據，包括原子類型、化學鍵和三維座標。一個關鍵挑戰在於整合這些不同形狀的模態，同時保持三維座標的SE(3)等變性。為實現這一目標，現有方法通常為不變性和等變性模態維護獨立的潛在空間，這降低了訓練和採樣的效率。在本研究中，我們提出了統一變分自編碼器用於三維分子潛在擴散建模（UAE-3D），這是一種多模態VAE，它將三維分子壓縮到統一潛在空間中的潛在序列，同時保持接近零的重建誤差。這種統一的潛在空間消除了在進行潛在擴散建模時處理多模態性和等變性的複雜性。我們通過採用擴散變壓器——一種沒有任何分子歸納偏置的通用擴散模型——來進行潛在生成，展示了這一點。在GEOM-Drugs和QM9數據集上的大量實驗表明，我們的方法在從頭生成和條件生成三維分子方面顯著建立了新的基準，實現了領先的效率與質量。

English

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.