RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908588454182912 |
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| author | Ma, Runze Zhang, Zhongyue Wang, Zichen Hua, Chenqing Rao, Jiahua Zhou, Zhuomin Zheng, Shuangjia |
| author_facet | Ma, Runze Zhang, Zhongyue Wang, Zichen Hua, Chenqing Rao, Jiahua Zhou, Zhuomin Zheng, Shuangjia |
| contents | Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions. While generative models are advancing biomolecule design, existing methods for designing RNA that target specific ligands face limitations in capturing RNA's conformational flexibility, ensuring structural validity, and overcoming data scarcity. To address these challenges, we introduce RiboFlow, a synergistic flow matching model to co-design RNA structures and sequences based on target molecules. By integrating RNA backbone frames, torsion angles, and sequence features in an unified architecture, RiboFlow explicitly models RNA's dynamic conformations while enforcing sequence-structure consistency to improve validity. Additionally, we curate RiboBind, a large-scale dataset of RNA-molecule interactions, to resolve the scarcity of high-quality structural data. Extensive experiments reveal that RiboFlow not only outperforms state-of-the-art RNA design methods by a large margin but also showcases controllable capabilities for achieving high binding affinity to target ligands. Our work bridges critical gaps in controllable RNA design, offering a framework for structure-aware, data-efficient generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching Ma, Runze Zhang, Zhongyue Wang, Zichen Hua, Chenqing Rao, Jiahua Zhou, Zhuomin Zheng, Shuangjia Biomolecules Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions. While generative models are advancing biomolecule design, existing methods for designing RNA that target specific ligands face limitations in capturing RNA's conformational flexibility, ensuring structural validity, and overcoming data scarcity. To address these challenges, we introduce RiboFlow, a synergistic flow matching model to co-design RNA structures and sequences based on target molecules. By integrating RNA backbone frames, torsion angles, and sequence features in an unified architecture, RiboFlow explicitly models RNA's dynamic conformations while enforcing sequence-structure consistency to improve validity. Additionally, we curate RiboBind, a large-scale dataset of RNA-molecule interactions, to resolve the scarcity of high-quality structural data. Extensive experiments reveal that RiboFlow not only outperforms state-of-the-art RNA design methods by a large margin but also showcases controllable capabilities for achieving high binding affinity to target ligands. Our work bridges critical gaps in controllable RNA design, offering a framework for structure-aware, data-efficient generation. |
| title | RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2503.17007 |