Equivariant Neural Diffusion for Molecule Generation

François R J Cornet, Grigory Bartosh, Mikkel N. Schmidt, Christian A. Naesseth

Keywords: Diffusion Models, Equivariant Neural Networks, 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.

Primary Area: Diffusion based models

Submission Number: 18516