RNAbpFlow：结合碱基对增强的 SE(3) 流匹配，用于条件 RNA 3D 结构生成
RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation
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Abstract
Despite the groundbreaking advances in deep learning-enabled methods for biomolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging owing to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology. Here we introduce RNAbpFlow, a sequence- and base pair-conditioned SE(3)-equivariant flow-matching model for generating RNA 3D structural ensembles. Leveraging a nucleobase center representation, RNAbpFlow enables end-to-end generation of all-atom RNA structures without the explicit or implicit use of evolutionary information or homologous structural templates. Experimental results show that base-pairing conditioning leads to broadly generalizable performance improvements over current approaches for RNA topology sampling and predictive modeling in large-scale benchmarking.
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