Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909177268404224 |
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| author | Lacombe, Romain Vaidya, Neal |
| author_facet | Lacombe, Romain Vaidya, Neal |
| contents | Recent advances in fast sampling methods for diffusion models have demonstrated significant potential to accelerate generation on image modalities. We apply these methods to 3-dimensional molecular conformations by building on the recently introduced GeoLDM equivariant latent diffusion model (Xu et al., 2023). We evaluate trade-offs between speed gains and quality loss, as measured by molecular conformation structural stability. We introduce Equivariant Latent Progressive Distillation, a fast sampling algorithm that preserves geometric equivariance and accelerates generation from latent diffusion models. Our experiments demonstrate up to 7.5x gains in sampling speed with limited degradation in molecular stability. These results suggest this accelerated sampling method has strong potential for high-throughput in silico molecular conformations screening in computational biochemistry, drug discovery, and life sciences applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models Lacombe, Romain Vaidya, Neal Quantitative Methods Machine Learning Recent advances in fast sampling methods for diffusion models have demonstrated significant potential to accelerate generation on image modalities. We apply these methods to 3-dimensional molecular conformations by building on the recently introduced GeoLDM equivariant latent diffusion model (Xu et al., 2023). We evaluate trade-offs between speed gains and quality loss, as measured by molecular conformation structural stability. We introduce Equivariant Latent Progressive Distillation, a fast sampling algorithm that preserves geometric equivariance and accelerates generation from latent diffusion models. Our experiments demonstrate up to 7.5x gains in sampling speed with limited degradation in molecular stability. These results suggest this accelerated sampling method has strong potential for high-throughput in silico molecular conformations screening in computational biochemistry, drug discovery, and life sciences applications. |
| title | Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2404.13491 |