Geometry-Complete Diffusion for 3D Molecule Generation

Alex Morehead, Jianlin Cheng

Keywords: Denoising diffusion probabilistic models, generative modeling, geometric deep learning, 3D molecule generation, computational biology

TL;DR: We introduce GCDM, a state-of-the-art Geometry-Complete SE(3)-Equivariant Diffusion Model for 3D molecule generation.

Abstract: Denoising diffusion probabilistic models (DDPMs) have recently taken the field of generative modeling by storm, pioneering new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to structure-guided protein design. Along this latter line of research, methods such as those of Hoogeboom et al. 2022 have been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a DDPM framework. Toward this end, we propose GCDM, a geometry-complete diffusion model that achieves new state-of-the-art results for 3D molecule diffusion generation by leveraging the representation learning strengths offered by GNNs that perform geometry-complete message-passing. Our results with GCDM also offer preliminary insights into how physical inductive biases impact the generative dynamics of molecular DDPMs. The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/Bio-Diffusion.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2302.04313/code)