Equivariant Neural Diffusion for Molecule Generation
François R J Cornet, Grigory Bartosh, Mikkel N. Schmidt, Christian A. Naesseth
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.
BibTeX
@inproceedings{
cornet2024equivariant,
title={Equivariant Neural Diffusion for Molecule Generation},
author={Fran{\c{c}}ois R J Cornet and Grigory Bartosh and Mikkel N. Schmidt and Christian A. Naesseth},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=40pE5pFhWl}
}