Unraveling the Potential of Diffusion Models in Small Molecule Generation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912475592523776 |
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| author | Zhang, Peining Baker, Daniel Song, Minghu Bi, Jinbo |
| author_facet | Zhang, Peining Baker, Daniel Song, Minghu Bi, Jinbo |
| contents | Generative AI presents chemists with novel ideas for drug design and facilitates the exploration of vast chemical spaces. Diffusion models (DMs), an emerging tool, have recently attracted great attention in drug R\&D. This paper comprehensively reviews the latest advancements and applications of DMs in molecular generation. It begins by introducing the theoretical principles of DMs. Subsequently, it categorizes various DM-based molecular generation methods according to their mathematical and chemical applications. The review further examines the performance of these models on benchmark datasets, with a particular focus on comparing the generation performance of existing 3D methods. Finally, it concludes by emphasizing current challenges and suggesting future research directions to fully exploit the potential of DMs in drug discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08005 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unraveling the Potential of Diffusion Models in Small Molecule Generation Zhang, Peining Baker, Daniel Song, Minghu Bi, Jinbo Biomolecules Artificial Intelligence Machine Learning Generative AI presents chemists with novel ideas for drug design and facilitates the exploration of vast chemical spaces. Diffusion models (DMs), an emerging tool, have recently attracted great attention in drug R\&D. This paper comprehensively reviews the latest advancements and applications of DMs in molecular generation. It begins by introducing the theoretical principles of DMs. Subsequently, it categorizes various DM-based molecular generation methods according to their mathematical and chemical applications. The review further examines the performance of these models on benchmark datasets, with a particular focus on comparing the generation performance of existing 3D methods. Finally, it concludes by emphasizing current challenges and suggesting future research directions to fully exploit the potential of DMs in drug discovery. |
| title | Unraveling the Potential of Diffusion Models in Small Molecule Generation |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.08005 |