Title: RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching
Venue: NeurIPS 2025
Accepted: yes
Keywords: Flow Matching

Generative Model

Biomolecule
Abstract: 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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an approach with the introduction of RiboFlow, a synergistic flow matching model designed to co-design RNA structures and sequences conditioned on target molecules. The authors curate RiboBind, a comprehensive dataset derived from the PDB database, which stands as the largest RNA-ligand interaction dataset to date. Experimental results are promising, demonstrating that RiboFlow achieves superior validity in unconditional RNA generation when compared to RNA-FrameFlow, while maintaining comparable performance across other key metrics. The following experiments validate some of the design choices.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. This paper proposes a novel idea by incorporating RNA backbone, structure and sequence features into a unified flow-based framework. The results are promising, particularly for ligand-conditioned RNA generation.\n\nWeaknesses\n1. The technical contribution seems incremental. The proposed method builds upon established works such as RNA-FrameFlow[1] and DFM[2], it would be beneficial for the authors to more clearly articulate the unique technical contributions of this work. For instance, the formulation of RNA frames and structure modeling in SE(3) space are derived from RNA-FrameFlow, and the mapping of discrete sequence tokens to continuous flow from DFM, besides the integration of these elements, are there any unique technical contribution of the this work ? Please introduce them in details.\n2. The fundamental design choice is not very well validated. The choice of a flow-based method for RNA design, while effective, could benefit from a more thorough justification, especially given existing methods like RNAFlow[3]. It would be helpful if the authors could elaborate on why a new flow-based framework is necessary and how it addresses limitations in current approaches. Additionally, since AlphaFold3 supports RNA-ligand interaction prediction and is used in this work's evaluation, it might be worth discussing why not replace RosettaFold2NA with AlphaFold3, which could potentially extend RNAFlow to RNA-ligand cases.\n3. The presentation and structure of the paper could be improved for clarity. While the authors claim three technical contributions—synergistic flow-matching, the RiboBind dataset, and a multi-faceted evaluation pipeline—the distribution of content seems uneven. The model design sections, such as Preliminary and 4.2, are detailed but largely based on previous works[1][2]. In contrast, the dataset (RiboBind) and evaluation protocol receive less explanation, which might underrepresent their significance. It would be valuable to see more novelty in the dataset curation and evaluation design. Additionally, some technical claims, such as “RiboFlow explicitly models RNA’s dynamic conformations while enforcing sequence-structure consistency to improve validity,” could be better supported by the model design description. For instance, in section 4.4, the focus is on modeling discrete nucleotides into flow, but it is not clear how sequence-structure consistency is explicitly enforced. Furthermore, the role of section 4.5 is somewhat ambiguous; it would be helpful to understand its integration into the overall pipeline—whether it generates ligand-guided RNA backbone inputs or refines post-prediction structures. Clarifying these aspects would strengthen the paper’s coherence and impact.\n\nIn light of the points discussed, I believe the paper would benefit from further revision to address the concerns raised, particularly in terms of clarifying the unique contributions, justifying the methodological choices, and improving the overall structure and presentation. While the current version may not fully meet the standards for a NeurIPS publication, the work shows promise and, with the suggested improvements, could be a strong candidate for future conferences. I encourage the authors to carefully consider these suggestions and continue their efforts in this area of research, which has potential applications in therapeutics and synthetic biology.\n\n[1] Flow Matching for de novo 3D RNA Backbone Design. \n\n[2] Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. ICML 2024\n\n[3] RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching. ICML 2024\n\nQUESTIONS:\n1. I was wondering if you could clarify the meaning of $ p_t $ in equation (1) within the context of RNA design. While the mathematical formulation is provided, a brief explanation of its role in the RNA-specific application would be helpful for better understanding.\n2. I'm interested in understanding the scale of the RiboBind dataset. With 1,591 RNA-ligand complexes and 3,012 RNA-ligand pairs, how does this compare to other existing datasets in the field? Is this considered large-scale and comprehensive for RNA-ligand interaction studies?\n3. Looking at Table 2, I see that the pre-training strategy appears to yield the best results among the three strategies presented. Could you explain why the dynamic cropping strategy is still important or how it contributes to the overall effectiveness of the model, even if it doesn't outperform pre-training in most metrics?\n\nLIMITATIONS:\n1. I am impressed by the approach of RiboFlow in tackling the challenges of ligand-conditioned RNA generation, particularly its ability to model RNA’s dynamic conformations and achieve high binding affinity. Given the flexibility of the flow-matching framework, I was wondering if there are plans to explore its application to other types of conditioned RNA generation, such as protein-conditioned RNA design or unconditioned RNA generation, to broaden its scope as a versatile tool for RNA design?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the authors propose RiboFlow, a generative framework for de novo co-design of RNA sequences and 3D structures explicitly conditioned on interacting small-molecule ligands. RiboFlow integrates SE(3)-equivariant flow matching across spatial coordinates, torsion angles, and discrete nucleotide types with a hierarchical ligand-conditioning module. The model is pretrained on RNAsolo and fine-tuned on a curated RiboBind dataset. It achieves up to 37 % validity in unconditional generation and, when provided ligand context, boosts validity by roughly 50 % over baselines, alongside up to a 2.2× improvement in binding-affinity metrics compared to RNA-FrameFlow.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nRiboFlow is a new deep learning method for de novo co‐design of RNA sequence and 3D structure conditioned on small-molecule ligands, and the authors support it with a newly curated RiboBind dataset with rigorous evaluation, including structural validity, sequence recovery, binding-affinity improvements, and Alphafold3 evaluation.\n\nWeaknesses:\n\nAlthough the paper stresses the importance of capturing RNA flexibility during ligand binding, it is not clear why the proposed method better captures dynamic structure. The authors mention that their parameterized SE(3)-equivariant flows can effectively model conformational flexibility, but they do not explain why this parameterization outperforms other approaches. An ablation study or additional experiments on RNAs with known large-scale motions would help demonstrate the method’s ability to capture structural uncertainty.\n\nThere is only one baseline model evaluated. Although RiboFlow targets a novel co-design task, additional baselines should be considered. For example, by fixing the RNA structure, the problem reduces to RNA inverse folding. RNA inverse folding has several well-established methods[1][2]. Comparing RiboFlow against existing inverse-folding algorithms would clarify whether the joint sequence–structure design truly outperforms traditional inverse-folding approaches.\n\n\n[1]Nori, Divya, and Wengong Jin. \"Rnaflow: Rna structure & sequence design via inverse folding-based flow matching.\" arXiv preprint arXiv:2405.18768 (2024).\n\n[2]Joshi, Chaitanya K., et al. \"grnade: Geometric deep learning for 3d rna inverse design.\" bioRxiv (2025): 2024-03.\n\nQUESTIONS:\n1. Could the authors clarify how RiboFlow effectively captures RNA conformational flexibility?  Modeling RNA’s dynamic structure is critical for accurate design: for example, RNAFlow[1] samples alternative structures during sequence generation to capture uncertainty, and ensemble-based methods like gRNAde[2] embed multiple conformations to account for flexibility. Although RiboFlow addresses a novel co-design task with few direct baselines, it remains unclear whether it outperforms these established approaches in modeling dynamic RNA structures. Perhaps ablation studies or targeted experiments will help.\n\n\n2. Although backbone-only parameterizations, built on a local coordinate frame defined by the ribose C4′, C3′ and O4′ atoms plus eight torsion angles, accurately capture large-scale RNA flexibility, they inherently omit the orientations and chemical identities of nucleobase edges that mediate specific ligand contacts[3]. Did you try full-atom RNA models? Full-atom moel explicitly include nucleobase positions, solvation and metal coordination, are therefore essential for faithfully recapitulating the chemophysical landscape of RNA–ligand interactions.\n\n3. Could the authors clarify why the rotation diffusion schedule is linear during training but exponential during sampling, and whether the RNA sequence sampling schedule follows a similar or different trajectory? How are the four variable types—translations, rotations, torsions, and nucleotide types—balanced during sampling, given that for t between 0 and 0.5 (and even up to t≈0.8) the RNA structure remains far from a valid fold, yet approximately 80 % of the sequence has already been sampled?\n\n[1]Nori, Divya, and Wengong Jin. \"Rnaflow: Rna structure & sequence design via inverse folding-based flow matching.\" arXiv preprint arXiv:2405.18768 (2024).\n\n[2]Joshi, Chaitanya K., et al. \"grnade: Geometric deep learning for 3d rna inverse design.\" bioRxiv (2025): 2024-03.\n\n[3]Stefaniak, Filip, et al. \"Modeling of ribonucleic acid–ligand interactions.\" Wiley Interdisciplinary Reviews: Computational Molecular Science 5.6 (2015): 425-439.\n\nLIMITATIONS:\nA key limitation of this work is the narrow set of comparisons—only RNA-FrameFlow is evaluated. While RiboFlow addresses a novel co-design task, it would be valuable to benchmark it against established RNA inverse-folding methods: by fixing the target structure, the problem reduces to inverse folding, for which several mature tools exist. Such comparisons would more convincingly demonstrate whether joint sequence–structure co-design offers real advantages over traditional structure-fixed approaches.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors design a pipeline using flow matching to achieve ligand-specific RNA structure generation.\nRiboFlow follows the framework of structure/sequence codesign in the protein domain and demonstrates strong performance with various evaluation metrics. \n\nThe main contributions are therefore in the form of designing RNA-specific frame representations\nThe authors also introduce RiboBind dataset to address the data paucity issue.\n\nSTRENGTHS AND WEAKNESSES:\n### Strength\n\nThe overall design of the pipeline is solid and is backed by strong mathematical theories. The codes are made public and well documented. \nPerformance remains modest but this is to be expected in such a low data setting.\n\n### Weakness\n\n1. Flow on labels seemed off, campbell 2024 used a rate matrix with CTMC formulation, where RiboFlow seemed using a linear interpolation. Did not find explicit theory in the orig paper that said such a linear interpolation is fine. (Eq 11 - 13 in RiboFlow, details of rate matrix around P42 in https://arxiv.org/pdf/2402.04997)\n* Fig 6. SPM and SPD are spermine ligands which are artefacts of crystallization\n* Line 538 \"Hairboss\" -> \"Hariboss\"\n* Many RNA-small molecule complexes are shaped by tight interactions with proteins which could bias the conformation space. Do the authors filter these complexes out of the dataset?\n* Line 228 - Many ligands share high chemical/physical similarity, only removing ligands by identity will still lead to leakage. Authors should report ligand similarity across splits.\n* Interaction prediction models are known to suffer from a lack of specificity (see https://doi.org/10.1021/acs.jmedchem.2c00487). Models tend to ignore the target and make predictions based on the ligand in the protein-ligan affinity case. I would expect a similar bias will operate in ligand-conditioned generation. Authors should report the degree to which designs are specific to the provided ligand. This can be easily tested by conditioning on different ligands and observing the structural variability it induces in the design. This would not be picked up by evaluations based on recovering 'ground truth' complexes (table 3). My fear is that due to the low data setting, models will simply tend to predict the most likely RNA independent of the ligand.\n* There have been other datasets curating RNA-ligand interactions, authors should discuss these and position theirs against them:\n    * PDBbind\n    * https://doi.org/10.1038/s41467-025-57852-0\n\nQUESTIONS:\n1. (Result iii in line 277) Validity of RiboFlow does not go up with the sampling steps as claimed.\n\n### Reference\n\nAndrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola. Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design. arXiv preprint arXiv:2402.04997, 2024.\n\nLIMITATIONS:\nsee above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}