Training-Free Guidance for Discrete Diffusion Models for Molecular Generation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910599932280832 |
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| author | Kerby, Thomas J. Moon, Kevin R. |
| author_facet | Kerby, Thomas J. Moon, Kevin R. |
| contents | Training-free guidance methods for continuous data have seen an explosion of interest due to the fact that they enable foundation diffusion models to be paired with interchangable guidance models. Currently, equivalent guidance methods for discrete diffusion models are unknown. We present a framework for applying training-free guidance to discrete data and demonstrate its utility on molecular graph generation tasks using the discrete diffusion model architecture of DiGress. We pair this model with guidance functions that return the proportion of heavy atoms that are a specific atom type and the molecular weight of the heavy atoms and demonstrate our method's ability to guide the data generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training-Free Guidance for Discrete Diffusion Models for Molecular Generation Kerby, Thomas J. Moon, Kevin R. Machine Learning Chemical Physics Biomolecules Training-free guidance methods for continuous data have seen an explosion of interest due to the fact that they enable foundation diffusion models to be paired with interchangable guidance models. Currently, equivalent guidance methods for discrete diffusion models are unknown. We present a framework for applying training-free guidance to discrete data and demonstrate its utility on molecular graph generation tasks using the discrete diffusion model architecture of DiGress. We pair this model with guidance functions that return the proportion of heavy atoms that are a specific atom type and the molecular weight of the heavy atoms and demonstrate our method's ability to guide the data generation. |
| title | Training-Free Guidance for Discrete Diffusion Models for Molecular Generation |
| topic | Machine Learning Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2409.07359 |