Equivariant Neural Diffusion for Molecule Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916791915118592 |
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| author | Cornet, François Bartosh, Grigory Schmidt, Mikkel N. Naesseth, Christian A. |
| author_facet | Cornet, François Bartosh, Grigory Schmidt, Mikkel N. Naesseth, Christian A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10532 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equivariant Neural Diffusion for Molecule Generation Cornet, François Bartosh, Grigory Schmidt, Mikkel N. Naesseth, Christian A. Machine Learning 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. |
| title | Equivariant Neural Diffusion for Molecule Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.10532 |