Abstract
Despite the groundbreaking advances in deep learning-enabled methods for bimolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging due to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology. We introduce RNAbpFlow, a novel sequence- and base-pair-conditioned SE(3)-equivariant flow matching model for generating RNA 3D structural ensemble. 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. RNAbpFlow is freely available at https://github.com/Bhattacharya-Lab/RNAbpFlow .
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