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arxiv 2407.19838 v2 pith:P2MYEYS4 submitted 2024-07-29 q-bio.BM cs.LG

RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching

classification q-bio.BM cs.LG
keywords designrnacgsequenceconditionaldiversegenerationuniversalfamily-specific
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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RNA plays a pivotal role in diverse biological processes, ranging from gene regulation to catalysis. Recent advances in RNA design, such as RfamGen, Ribodiffusion and RDesign, have demonstrated promising results, with successful designs of functional sequences. However, RNA design remains challenging due to the inherent flexibility of RNA molecules and the scarcity of experimental data on tertiary and secondary structures compared to proteins. These limitations highlight the need for a more universal and comprehensive approach to RNA design that integrates diverse annotation information at the sequence level. To address these challenges, we propose RNACG (RNA Conditional Generator), a universal framework for RNA sequence design based on flow matching. RNACG supports diverse conditional inputs, including structural, functional, and family-specific annotations, and offers a modular design that allows users to customize the encoding network for specific tasks. By unifying sequence generation under a single framework, RNACG enables the integration of multiple RNA design paradigms, from family-specific generation to tertiary structure inverse folding.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GoForth: Language Models for RNA Design under Structure, Sequence, and Coding Constraints

    q-bio.QM 2026-05 unverdicted novelty 7.0

    GoForth is a forward-trained encoder-decoder RNA language model that generates sequences under mixed constraints on fold, sequence, and coding by separating sequence prior, forward folding sampler, and reward oracle.