Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
This work addresses the problem of efficient and scalable 3D molecule generation for computational chemistry and drug discovery, representing an incremental improvement over existing methods.
The paper tackles the scalability and efficiency limitations of equivariant diffusion models for 3D molecule generation by relaxing equivariance constraints, resulting in sample quality comparable to state-of-the-art equivariant models with improved training and sampling efficiency.
Equivariant diffusion models have achieved impressive performance in 3D molecule generation. These models incorporate Euclidean symmetries of 3D molecules by utilizing an SE(3)-equivariant denoising network. However, specialized equivariant architectures limit the scalability and efficiency of diffusion models. In this paper, we propose an approach that relaxes such equivariance constraints. Specifically, our approach learns a sample-dependent SO(3) transformation for each molecule to construct an aligned latent space. A non-equivariant diffusion model is then trained over the aligned representations. Experimental results demonstrate that our approach performs significantly better than previously reported non-equivariant models. It yields sample quality comparable to state-of-the-art equivariant diffusion models and offers improved training and sampling efficiency. Our code is available at https://github.com/skeletondyh/RADM