SE(3)-Stochastic Flow Matching for Protein Backbone Generation
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866929310053433344 |
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| author | Bose, Avishek Joey Akhound-Sadegh, Tara Huguet, Guillaume Fatras, Kilian Rector-Brooks, Jarrid Liu, Cheng-Hao Nica, Andrei Cristian Korablyov, Maksym Bronstein, Michael Tong, Alexander |
| author_facet | Bose, Avishek Joey Akhound-Sadegh, Tara Huguet, Guillaume Fatras, Kilian Rector-Brooks, Jarrid Liu, Cheng-Hao Nica, Andrei Cristian Korablyov, Maksym Bronstein, Michael Tong, Alexander |
| contents | The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions -- i.e. the group $\text{SE}(3)$ -- enabling accurate modeling of protein backbones. We first introduce FoldFlow-Base, a simulation-free approach to learning deterministic continuous-time dynamics and matching invariant target distributions on $\text{SE}(3)$. We next accelerate training by incorporating Riemannian optimal transport to create FoldFlow-OT, leading to the construction of both more simple and stable flows. Finally, we design FoldFlow-SFM, coupling both Riemannian OT and simulation-free training to learn stochastic continuous-time dynamics over $\text{SE}(3)$. Our family of FoldFlow, generative models offers several key advantages over previous approaches to the generative modeling of proteins: they are more stable and faster to train than diffusion-based approaches, and our models enjoy the ability to map any invariant source distribution to any invariant target distribution over $\text{SE}(3)$. Empirically, we validate FoldFlow, on protein backbone generation of up to $300$ amino acids leading to high-quality designable, diverse, and novel samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02391 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SE(3)-Stochastic Flow Matching for Protein Backbone Generation Bose, Avishek Joey Akhound-Sadegh, Tara Huguet, Guillaume Fatras, Kilian Rector-Brooks, Jarrid Liu, Cheng-Hao Nica, Andrei Cristian Korablyov, Maksym Bronstein, Michael Tong, Alexander Machine Learning Artificial Intelligence The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions -- i.e. the group $\text{SE}(3)$ -- enabling accurate modeling of protein backbones. We first introduce FoldFlow-Base, a simulation-free approach to learning deterministic continuous-time dynamics and matching invariant target distributions on $\text{SE}(3)$. We next accelerate training by incorporating Riemannian optimal transport to create FoldFlow-OT, leading to the construction of both more simple and stable flows. Finally, we design FoldFlow-SFM, coupling both Riemannian OT and simulation-free training to learn stochastic continuous-time dynamics over $\text{SE}(3)$. Our family of FoldFlow, generative models offers several key advantages over previous approaches to the generative modeling of proteins: they are more stable and faster to train than diffusion-based approaches, and our models enjoy the ability to map any invariant source distribution to any invariant target distribution over $\text{SE}(3)$. Empirically, we validate FoldFlow, on protein backbone generation of up to $300$ amino acids leading to high-quality designable, diverse, and novel samples. |
| title | SE(3)-Stochastic Flow Matching for Protein Backbone Generation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.02391 |