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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.17340 |
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| _version_ | 1866911016375287808 |
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| author | Ni, Yuyan Feng, Shikun Ma, Wei-Ying Ma, Zhi-Ming Lan, Yanyan |
| author_facet | Ni, Yuyan Feng, Shikun Ma, Wei-Ying Ma, Zhi-Ming Lan, Yanyan |
| contents | Sampling strategies in diffusion models are critical to molecular generation yet remain relatively underexplored. In this work, we investigate a broad spectrum of sampling methods beyond conventional defaults and reveal that sampling choice substantially affects molecular generation performance. In particular, we identify a maximally stochastic sampling (StoMax), a simple yet underexplored strategy, as consistently outperforming default sampling methods for generative models DDPM and BFN. Our findings highlight the pivotal role of sampling design and suggest promising directions for advancing molecular generation through principled and more expressive sampling approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting Sampling Strategies for Molecular Generation Ni, Yuyan Feng, Shikun Ma, Wei-Ying Ma, Zhi-Ming Lan, Yanyan Chemical Physics Sampling strategies in diffusion models are critical to molecular generation yet remain relatively underexplored. In this work, we investigate a broad spectrum of sampling methods beyond conventional defaults and reveal that sampling choice substantially affects molecular generation performance. In particular, we identify a maximally stochastic sampling (StoMax), a simple yet underexplored strategy, as consistently outperforming default sampling methods for generative models DDPM and BFN. Our findings highlight the pivotal role of sampling design and suggest promising directions for advancing molecular generation through principled and more expressive sampling approaches. |
| title | Revisiting Sampling Strategies for Molecular Generation |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2506.17340 |