Papers › Equivariant Neural Diffusion for Molecule Generation

Equivariant Neural Diffusion for Molecule Generation

12 Jun 2025arXiv:2506.10532archive 2025-07-28

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

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.

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