Equivariant Neural Diffusion for Molecule Generation
Abstract
We introduce Equivariant Neural Diffusion (END), a novel diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Compared to current state-of-the-art equivariant diffusion models, the key innovation in END lies in its learnable forward process for enhanced generative modelling. Rather than pre-specified, the forward process is parameterized through a time- and data-dependent transformation that is equivariant to rigid transformations. Through a series of experiments on standard molecule generation benchmarks, we demonstrate the competitive performance of END compared to several strong baselines for both unconditional and conditional generation.
Cite
Text
Cornet et al. "Equivariant Neural Diffusion for Molecule Generation." Neural Information Processing Systems, 2024. doi:10.52202/079017-1564Markdown
[Cornet et al. "Equivariant Neural Diffusion for Molecule Generation." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/cornet2024neurips-equivariant/) doi:10.52202/079017-1564BibTeX
@inproceedings{cornet2024neurips-equivariant,
title = {{Equivariant Neural Diffusion for Molecule Generation}},
author = {Cornet, François and Bartosh, Grigory and Schmidt, Mikkel N. and Naesseth, Christian A.},
booktitle = {Neural Information Processing Systems},
year = {2024},
doi = {10.52202/079017-1564},
url = {https://mlanthology.org/neurips/2024/cornet2024neurips-equivariant/}
}