Diffusion Models for Molecules: A Survey of Methods and Tasks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916613074190336 |
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| author | Wang, Liang Song, Chao Liu, Zhiyuan Rong, Yu Liu, Qiang Wu, Shu Wang, Liang |
| author_facet | Wang, Liang Song, Chao Liu, Zhiyuan Rong, Yu Liu, Qiang Wu, Shu Wang, Liang |
| contents | Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant attention. In recent years, diffusion models have emerged as an impressive class of deep generative models, sparking extensive research and leading to numerous studies on their application to molecular generative tasks. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. Particularly, due to the diversity of diffusion model formulations, molecular data modalities, and generative task types, the research landscape is challenging to navigate, hindering understanding and limiting the area's growth. To address this, this paper conducts a comprehensive survey of diffusion model-based molecular generative methods. We systematically review the research from the perspectives of methodological formulations, data modalities, and task types, offering a novel taxonomy. This survey aims to facilitate understanding and further flourishing development in this area. The relevant papers are summarized at: https://github.com/AzureLeon1/awesome-molecular-diffusion-models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion Models for Molecules: A Survey of Methods and Tasks Wang, Liang Song, Chao Liu, Zhiyuan Rong, Yu Liu, Qiang Wu, Shu Wang, Liang Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant attention. In recent years, diffusion models have emerged as an impressive class of deep generative models, sparking extensive research and leading to numerous studies on their application to molecular generative tasks. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. Particularly, due to the diversity of diffusion model formulations, molecular data modalities, and generative task types, the research landscape is challenging to navigate, hindering understanding and limiting the area's growth. To address this, this paper conducts a comprehensive survey of diffusion model-based molecular generative methods. We systematically review the research from the perspectives of methodological formulations, data modalities, and task types, offering a novel taxonomy. This survey aims to facilitate understanding and further flourishing development in this area. The relevant papers are summarized at: https://github.com/AzureLeon1/awesome-molecular-diffusion-models. |
| title | Diffusion Models for Molecules: A Survey of Methods and Tasks |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2502.09511 |