RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching
Authors: Runze Ma
Zhongyue Zhang
Zichen Wang
Will Hua
Jiahua Rao
Zhuomin Zhou
Shuangjia Zheng
Venue: NeurIPS 2025 poster ()
Keywords: Flow Matching
Generative Model
Biomolecule
Primary area: machine_learning_for_sciences
TL;DR: We present RiboFlow, a flow-matching based model that co-designs RNA structures and sequences with high binding affinity to target molecules.

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.

RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching
Runze Ma
Zhongyue Zhangl., Zichen Wang', Chenging Hua", Jiahua Rao Zhuomin Zhou', Shuangjia Zheng"
Shanghai Jiao Tong University Yale University Sun Yat-Sen University Lingang Laboratory Equal contribution Corresponding Author
Abstract
Ribonucleic acid (RNA) binds 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 enforeing sequence- -trrucccettructure structure consistency to improve validity. Additionally, we curate RiboBind, 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 -frt---rttt--ttttt RNA design methods by 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 ttructure-aware data-efficient generation.
Introduction
Ribonueleic acid (RNA) is a programmable biomolecule that achieves precise molecular recognition through its dynamic ftree-dimensione structure [44), enabling applications in catalysis, biosensing, and therapeutic targeting [43, 7]. Advances computational tools, exemplified by AlphaFold3 [1]. have revolutionized biomolecular structure prediction, while generative models [27, 48, 21, 42] now pioneer the design of de novo biomolecules with specific binding properties. RNA, with its structural flexibility at the tertiary level, ease of chemical synthesis in laboratory settings, and low immunogenicity in biological systems, stands out promising candidate for therapeutic drugs and biochemical reagents [37, 12]. However, existing methods for designing RNAs that bind specific small molecules- criticall for therapeutic and diagnostic applications- face unresolved challenges at the intersection of data availability, interaction modeling, and structural validity.
Recent work has laid foundations for RNA design. Tools like RNAiFold [13] and gRNAde [24] generate sequences matching predefined secondary tertiary structures, while RNA- ANA-FrameFlow [3]. MMDiff [31], and RNAFlow [32] focus on backbone generation. Yet. designing RNA for small- molecule targeting remains an open problem due to three gaps: (1) the inherent conformational flexibility of RNA requires the simultaneous and consistent consideration of both its structure and sequence [15]; (2) existing models lack explicit conditioning on ligand geometry, limiting their ability to capture RNA-li binding dynamics; and (3) the scarcity of RNA-ligand structural data restricts the scalability and generalizability of data-driven approaches.
To bridge these gaps, we identify three key challenges. First, ensuring generated RNAs satisfy both binding specificity and biophysical validity requires co-designing sequence and structure in
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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synergistic framework. Second, modeling the structural flexibility of RNA, especially its torsion angles and backbone dynamics, while maintaining sequence-structure compatibility, remains complex problem involving both geometric and thermodynamic considerations. Third, the absence of large-scale, standardized RNA-ligand interaction datasets hinders training robust generative models.
We try to address these challenges. For model design, we introduce RiboFlow, a synergistic flow matching model for de novo RNA discrete sequence and continuous structure co-design. By conditioning on ligand geometry and leveraging RNA backbone frames, torsion angles, and sequence features, RiboFlow models conformational flexibility while enforcing sequence-structuue consistency. A novel co-design pre-training strategy is proposed to further enhance geometric awareness by distilling structural priors from RNA crystal structures. To overcome data scarcity, we introduce RiboBind, a comprehensive dataset of RNA-ligam complexes systematically curated from the PDB database, comprising 1,591 RNA-ligand complexes and 3,012 RNA-ligand pairs.
We propose a first-of-its-king synergistic flow matthig framework for ligand-conditioned de novo RNA design. The model incorporates torsion angle and backbone frame modeling, enabling sequence-structure co-design for specified ligands while offering controllable ligand-binding specificity. (ii) Dataset: We present RiboBind, large standardized RNA-ligand interaction benchmark, enabling data-driven RNA discovery. (iii) Evaluation: We develop a multi-facete pipeline assessing structural validity and binding affinity (via docking and scoring). Experimental results demonstrate that RiboFlow outperforms state-of state-of-the of-the-art RNA design methods by a large margin (e.g., achieving a 2.2-fold improvement in the AF3 binding metric and 50% increase in validity). but also showcases controllable capabilities for achieving high binding affinity to target ligands. We anticipate this work advancing RNA design toward structure-aware, ligand-conditioned design, with promising applications in therapeutics and synthetic biology.
Our contributions are: (i) Task Formulation:
2 Related Work
2.1 RNA Design
Currently, RNA design can be broadly categorized into main approaches: sequence-based and structure-based methods. Sequence-basee design primarily aims to address the RNA inverse folding problem, which involves designing an RNA sequence that folds into a desired RNA structure. While early efforts [13, 46, 9, 34] largely focus on RNA secondary structure information, recent studies [38, 24, 22, 44] begin to explore the use of RNA 3D structural information to guide sequence design. On the other hand, structure- baased design, still in its early stages, encompasses approaches such as RNA backbone design [3]. which enables the creation of RNA with specific structures. Another approach is -potein co-design [31, 32), which focuses on designing RNA and protein components by accounting for their interactions. However, these methods are not capable of designing ligand- targeting RNAs, limiting their applications in broader diagnostic and synthetic biology scenarios.
2.2 Flow Matching
Flow matching [28, 29], an emerging generative modeling approach, is increasingly recognized as compelling alternative to traditional generative models [26, 18, 20]. It has extensive applications in both the computer vision and natural language processing fields [10, 16, 14). Within the realm of biomolecular design, the focus of this paper, researchers are actively exploring the application of flow matching for diverse molecular design tasks. For example, several studies [50, 47, 5] utilize
SE(3)-cquivariant flow models design small molecules and proteins, while others [23, 6] integrate protein sequence for protein design, or leverage multiple sequence alignment (MSA) coevolutionary information [21, 42] to design enzymes and antibodies. In contrast to these methods, RiboFlow integrates RNA-specific structural priors with sequence-structuue consistency through synergistic flow matching, thereby deriving the benefits of both data-efficient representations for RNA inputs.
3 Preliminary
RNA Backbone Parameterization. As illustrated in Figure 1, an RNA has backbone made of alternating phosphate groups (P, OP1, OP2, O5') and the sugar ribose (C1' C5', O2', O3', O4'). A nitrogen atom, located at the base attachment site (N9 of purines or N1 I of pyrimidines), is often

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included in modeling. Utilizing recent RNA parameterization methods [31, 3], we construct local coordinate system for each nucleotide based on the C4', C3', and O4' atoms of the ribose. The positions of the remaining atoms are then determined by set of eight torsion angles, Φ { i= where Φi E IR² along with bond lengths and bond angles [17]. This parameterized approach can effectively captures the conformational flexibility of RNA.
Notations and Problem Formulation. We consider a binding system comprising an RNA-ligand pair = CT (T.M), where the RNA, denoted by T. con- sists of N2 nucleotides, and the ligand, denoted by M, consists of Nn atoms. The 3D geometry of the ligand represented as M = {(al), (60) )())) Ji=1. where ( E Th indicate the atom type (nm the total number of possible atom types), and 6() R3 denotes its 3D Cartesian coordinates. The RNA represented by the backbone atoms of each nu- cleotide, and is parameterized as set of nucleotide blocks ( = (x((0 ((()) c( j. Here, (+) E R$ is the position of the C4'l atom of the i-th nucleotide, r()) E SO(3) is rotation matrix defining the orien- tation of the local frame formed by -3' 04'
RNA frame
10..0.....
Figure 1: RNA backbone parameterization. Using the C4' atom as the origin, a local co- ordinate system is established using and v2 defined by vectors along the C4'-04' and C4'- C3' bonds via the Gram-Schman process.
relative to global reference frame, and c E {A, C. G. U} represents the nucleotide type. The
complete RNA structure is represented by T = } Our goal is to develop a probabilistic model that learns the conditional distribution (T] L.e. generating RNA T conditioned target ligand structure M.
4 Methods
In this section, present RiboFlow, flow matching framework for de novo RNA design conditioned on target small molecules. RiboFlow operates in two key stages: initially, learns distribution of structurally plausible RNAs during synergistic co-design pre-training stage, establishing priors for molecule-conditione generation. Subxequently, it is trained to generate high-affinity RNAs given a specific target molecule. For clarity, comprehensive overview of the flow matching is discussed in the Appendix D, with mathematical tools and proof' used in our methodology. The architecture of RiboFlow is illustrated in Figure 2.
4.1 Overview
As mentioned in the preliminaries, the i-th nucleotide in an RNA can be parameterized as 7r()) (z=(), ,(0 c(+) ). We time to represent the target data (i.e., the real RNA data Ti), and =
to represent noise data (i.e., To). Inspired by recent work of [47, 6]. the conditional flow (T((TT in the RNA generation process for time step E [0, 1] can be expressed
N;
Ps (T.TTT) = II
pa
(I)
1=1
where N, denotes the length of the RNA sequence. The above formula allows us synergistically consider the probability distributions of the three parts (translation, rotation, and nucleotide type) during training and sampling, thereby modeling the RNA generation process into the SE(3) space and the nucleotide type space, respectively.
4.2 RiboFlow on SE(3)
We model RNA structure in SE(3) space using set of structure frames, denoted as F {(x(9) (0)) where (+) E R represents the translation and p(()) SO(3) represents the ro- tation of the i-th nucleotide. We define forward process that transforms an initial noisy frame set
Fo Po (FF)) to target structure frame set F1 (Fi). A continuous flow F, between Fo and Fi
3

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Data Curation
Pretraining and Finetuning
Generation
PDB
1A9E
ENAMteater Firrer
cuNS: Polo recations Vs (su))
TMI
Seeft Cor atrunt
Vold igand
@
Dietwnoo 5Av
AMEEAAAIIA
Fatractoo
Canditccal Gener atice
LNA-Ligand palro
CO CO Co
Cordd BoY
MICHAMMCAG
AUCGAUOOCA
Figure 2: Framework of RiboFlow. We start by collecting and constructing RiboBind, a large-scale RNA-ligand interaction dataset, from the RCSB PDB database. RiboFlow is then pretrained on RNAsolo using a sequence-structure synergistic co-design strategy to enhance its geometric awareness. Next, the model is fine-tuned with RiboBind, enabling it to design RNAs with high predicted affinity under ligand constraints. During inference, RiboFlow generates structurally valid and high-affinity RNAs tailored to different ligands. In the figure, "X" represents uncertain nucleotides.
is constructed by interpolating on the SE(3) manifold:
F. = expro (t (loo (Fi))-
Here, exp(-) and log(-) are the exponential and logarithmic maps respectively, enabling movement along the curved SE(3) manifold. Since the SE(3) can be decomposed into independent translations and rotations, we can also obtain the closed- fored-fomm interpolations [47] for IR3 and SO(3) separately:
(1 t) + tæ1:
r: = expro (t. logr, (r1));
where zo sampled from (0, I), and ro sampled uniformly from the rotation group SO(3), denoted as (3)) Based on these interpolations, we can derive the conditional vector fields un for the translation and rotation components respectively [39] due their simple nature:
11 o (8)
_
(8)
(5)
Ue (1) ( (i) log
(6)
Hence, we leverage an -quuivvraaa neural network 2y (.) ) to regress the conditional vector fields time f. For N₁ structure frames, the loss to train the conditional flow matching for translation can be written as follows:
Lirans
E (0110 po (mo) (2
Σ
(1) 0o
(1),
(6) +
(1) xi
Rs
and the loss to train rotation conditional flow matching is:
N
log, (0)
L (-1000 Po po(ro))pc(esllv.c
wo (r.,t)
S003
At the same time, according to Equations 5 and 6, we can obtain the predicted structure frame [(a()) (( given the corrupted structure frame Ft.
A
4.3 RiboFlow on Torsion Angles
The conformational flexibility of the RNA backbone largely determined by its 8 torsion angles. These angles, á E ④ which are elements of R2 require appropriate constraints to generate physically plausible RNA structures. Existing studies have explored modeling torsion angles on the torus [27] and demonstrated promising experimental results. However, such an approach may increase computational

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complexity when regressing conditional vector fields of torsion angles, Insipired by Anand er al. [3]. we introduce a torsion angle prediction module, denoted as v This module employs shallow ResNet architecture [19], which is first implemented in the structure prediction module of AlphaFold2 [25] and has been widely adopted in recent works. By utilizing the predicted structural
frames F1 = { .1 ((²)) ) the module can predict the ground-truth torsion angles Φi directly.
The process can be formulated as follows:
i
es (4 A(1)).
Here, (1) represents the predicted set of 8 torsion angles for the i-th nucleotide. The loss between the predicted and true torsion angles can be formulated as:
N.
Liors
Σ
Φ
(10)
8N,
R2
dEO (8) .0E (4)
4.4 RiboFlow Nucleotide Type
The intricate interplay between sequence and structure in RNA molecules necessitates a generation process that can effectively capture and leverage their mutual constraints. To address this, we introduce a synergistic flow matching approach that integrates the semantic information of the sequence with the structural flexibility inherent to RNA. This method not only constrains the generation process but also ensures that the generated sequences are biologically plausible and functionally relevant.
(c(*) } Ji=1 we define cf p( (2) where 8 is probability vector representing the distribution over nucleotide types, following multinomial distribution.
For RNA sequence
Following the work of [6]. we employ conditional flow to linearly interpolate from uniform prior (+) This requires that the probability vector satisfies 81 onehot(c;) and Sy C...., ....,,)) 2). The
conditional flow of the probability vector is then given by:
[8) + (1 ()80,
(11)
and the corresponding conditional vector field is:
(s)800 81) 81 80-
(12)
We use the neural network v- to predict the vector field of nucleotide types. The network's training objective is optimized by minimizing the following loss:
N
Ltype E (-)
Σ CE(s)) + (1=
(13)
pu(so).
where CE(-) is the cross-entropy function. This loss directly measures the difference between the true probability distribution and the inferred distribution for the RNA sequence.
4.5 Distence-Aware Ligand Guided RNA Generation
While the preceding sections detailed the unconditional RNA generation through the synergistic flow matching, it is still necessary incorporate ligand information during the training and sampling phases to guide the model toward producing RNA with specific ligand binding affinity. Therefore, we introduce hierarchical RNA-ligand interaction module that explicitly incorporates ligand information into the RNA generation process.
Given a ligand M = {(a(), 3 a two-stage distance-aware structure refinement process designed to implicitly optimize the binding free energy by modeling 3D RNA-ligand interactions. In the first stage, a multi-layer perceptron is employed to learn the embedding h, for atoms in the ligand. Subsequently, an invariant point attention (IPA) module predicts the initial RNA structural frame f {(0()) ) without considering ligand interaction. Next, We compute the spatial interaction feature ha between the i-th RNA backbone atom (i E [1, N.)) and the j-th ligand atom (j E [1, Nn.) as:
hb exp (->11a{) (0)))))
(14)
5

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where y is scaling factor controlling the spatial sensitivity. In the second stage, the IPA module refines the structure using hg and hng yielding the post-interaction RNA frame:
Fi IPA [1, Na, hn)-
(15)
This two-stage process produces a refined RNA structure conditioned on the specific ligand, enabling conditional flow matching optimization under ligand constraints.
4.6 Training and Inference.
Training. To fully leverage ligand information as conditional inputs, we incorporate the entire RNA-ligand complex input. Specifically, we sample zt, Vx, and ot alongside the ligand information through the defined conditional probability paths. Consequently, the complete binding complex at time serves as input to the vector field vr(-), represented ve(- (TIUUM) = Cc). Thus, the overall training loss can be expressed as:
Littal = Eo(Eas + Crot + Lrorsion + Laype).
(16)
At this point, the loss about translation can be expanded as:
Ltaass E (~1((0.1),p(z1), ||va (T; C) 21 + 1
(17)
Similarly, the losses for rotation and nucleotide type can be formulated analogously. With these defined, the model is ready for training under specific ligand conditions.
Inference. The generation for an RNA of length N is initiated by creating a random point cloud as a structure frame and a random RNA sequence of the same length. Besides, the 3D structure of a ligand is incorporated as the conditional input. This noisy frame and sequence are then iteratively refined into realistic RNA structure and sequence through the trained model De and an ODEsolver. The sampling process is detailed in the Algorithm 1.
5 Experiments
Pretraining Dataset. To establish foundational priors for RNA structural validity, we pre-train RiboFlow RNAsolo [2], curated database of single-stranded RNA 3D structures. We filter entries to those with resolution < 4 Å (as of December 2024) and retain sequences between 30-200 nucleotides to balance structural diversity with computational feasibility, yielding 7,154 high-quality training samples. This length range reflects typical functional RNA motifs while accommodating GPU memory constraints.
RNA -NA-ligand Interaction Dataset. We introduce RiboBind, large standardized dataset for RNA- small molecule interactions, addressing data scareity ligand-conditioned design. We discuss dataset construction and perform statistical analysis in the Appendix 1. We perform dynamic cropping and truncate regions distal to the ligand-binding pocket. preserving interaction sites while maximizing data utility (details discussed in Appendix A.3). Mcanwhile, we have conducted comprehensive and objective comparison of existing RNA-ligand datasets, which can be referred to in the Appendix A.2.
Dataset Splits. To evaluate model generalization, we employ the following partitioning strategies for RiboBind: (1) Sequence-baser -aase Evaluation: RNA sequences are clustered a 50% identity threshold using MMseqs2 [36]. A test set is formed from the cluster centroids, comprising 66 RNA-ligand pairs which include of the most frequently observed ligands. All non-centroid sequences constitute the corresponding training set. (2) Structure-bssed Evaluation: Following the methodology of gRNAde [24], RNA structures are clustered using US-align [49] with a TM-score threshold of 0.45, yielding 277 distinct structural classes. These classes are then partitioned into training set (249 classes) and a test set (28 classes) at a 9:1 ratio. The final test dataset for this evaluation consists of 28 RNA-ligand pairs, each formed by one representative structure from held-out test class and its corresponding ligand. (3) Few-shot Evaluation: Contains 15 Rigand pairs involving ligands that appear only once in the RiboBind dataset, designed evaluate low-resource generalization. In the main text, we primarily introduce sequence-bard based evaluation, while the appendix provides a detailed analysis for structure-based evaluation and few-shot evaluation.
Evaluation Metrics. We evaluate our experiment using metries for both generation quality and binding capability. For generation quality, we focus on four metries: Validity (val.), assessed

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by -oledigg each generated backbone with gRNAde [24] and predicting its structure using RhoFold [35] with generated sequences. The validity is determined by self-consistency TM-score (scTM) between the predicted and original backbone at the C4' level, with scTM 2 0.45 indicating a valid backbone; Diversity (dix.), measured by the proportion of unique structural clusters (identified by qTMMclust [49]) among valid samples, Structures with a TM-score 2 0.45 are considered similar, reflecting the structural variability of generated samples; and Novelty (nov.), evaluated using US- align [49] to compare the structure of valid backbones to the training distribution, with higher novelty indicating greater structural divergence. Sequence Recovery (SR.), quantified as the percentage of correctly recovered nucleotides in the co-designed sequence relative to the ground- truut RNA.
For binding capability. we assess the complex binding capability in-silico by three metrics: AF- Score (AF.), Alphafold3's 's overall prediction confidence for the complex, incorporating structural confidences (pTM and ip'TM), clash penalties, and considerations for disordered regions, where higher scores are better; GerNAScore (measured by GerNA-Bind )), A recently proposed model for RNA-ligand affinity prediction [45], where higher scores correspond to stronger predicted binding affinity. VinaScore (vina.), the binding free energy (in kcal/mol) between RNA and ligand predicted by AutoDock Vina [41], with lower values signifying stronger binding capacity.
Baselines. To the best of our knowledge, there is currently no RNA design model specifically targeting small molecules. RNAFlow [32] relies on TTAFoddA [4] to design RNA structures from predicted protein-RNA -eecinn-NNAAA complex structures, which cannot predict RNA- -liaand complex conformations. MMDiff i31] is also limited to protein- generation. LigandMPNN [11] can only generate proteins for specific ligands. To this end, we choose RNA-FrameFloo [3] for comparison as it is a general RNA structure generation model, despite not being constrained by ligands. For further comparison, we also include a baseline where sequences are randomly generated and folded using RhoFold.
RNA-ligand
5.1 Unconditional RNA Generation
Setup. We first evaluate the generative capabilities of each model without conditioning on ligand information. In addition to using the original RNAFrame-Flow model, we assess the effectiveness of the proposed co-design pre-training strategy by comparing: (i) ENNA-rameFloo-RR a retrained version of RNA-FrameFlow using the new pre-training dataset with RNA backbone frames and torsion angle loss; (ni RiboFlow, the proposed model trained with the sequence-structure co- design loss. For each model, we set the generation length in the range of [50, 150], sampling at intervals 20, and generate 100 samples for each length to cover the model's sampling space.
The experimental results are presented in Table where step_50 denotes a sampling step of 50. A more detailed comparison of model perfor- mance under varying pre-training steps is provided in Table 4. The local structural properties of RNAS generated by RiboFlow and RNAFrame-FFoon Flow-R are illustrated in Figure 8. We can draw the following conclusions: (i) The validity of RNAS generated by RiboFlow has shown significant improvement com- pared to the other baselines.
Results.
Table 1: Unconditional RNA generation.
METHOD &VA..(1)
Div.(t) Nov.(L)
RNA- FRAMFFAAFLLOW
STEP_50
25.0
0.573
0.582
STEP_100
30.3
0.503
0.594
ENA-FRAMEFLOW-B
(ii) Due to the addition of sequence feature constraints, the diversity of RNAs generated by the RiboFlow has slightly decreased compared to RNA-FrameFlow-RR (iii) Increasing the sampling steps can enhance the validity of RNA struc- tures to certain extent. Besides, it can also be noted that the structures generated by RiboFlow are close to the real structures in terms of bond lengths and torsion angles in Figure 8.
STEP_50
26.3
0.577
0.562
STEP_100
30.7
0.530
0.546
RIBOFLOW
STEP _50 STEP_100
34.7
0.550
0.540
31.9
0.545
0.577
5.2 RNA Generation Guided by Ligand Structure
We evaluate the performance of models conditioned on ligand-bound structures and further examine the impact of incorporating the ground-truth RNA sequence length as prompt. Hence, we design
htttps//ggitee www./EENTLE----- Law.laaaaae--reaaeeeeee Bind
7

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Table 2: Comparison of RNA generation with sequence- sequence-based evaluation.
VINA. (4)
GERNA. (T)
SAF (1)
VE DIv.(1) Nov.
TOP10
MEDIAN
GENERATION GIVEN LIGAND STRUCTURE AND TRUE LENGTH
RANDOM
-2.76
-2.55
0.101
3.11
UNA-FRAMEFLOW
-4.15
-4.01
0.203
17.4
22.5
0,288
0.584
RIBOFLOW
.4.25
-4.12
0.283
22.8
10.8
0.226
0.640
PRE
-4.39
-4.31
0.437
48.4
26.8
0.354
0.514
CROP
-4.38
-4.27
0.402
34.7
12.1
0.293
0.603
PRE CROP
-4.30
-4.18
0,411
50.1
24.2
0.376
0.534
GENERATION GIVEN LIGAND STRUCTURE
SAMPLING LENGTH
RANDOM
-2.91
-2,82
0.113
5.25
RNA-FRAMEFLOW
-4.27
-4.15
0.209
22.7
0.545
0.574
RIBOFLOW
-4.61
-4.48
0.297
25.7
10.8
0,517
0.669
PRE
-4.55
0.467
51.2
25.9
0.575
0.519
CROP
-4.56
0.456
38.9
12.7
0.522
0.613
PRE CROP
-4.58
-4.43
0.448
54.8
25.6
0.589
0.527
GENERATION GIVEN LIGAND SMILES AND SAMPLING LENGTH
RANDOM
-3.32
-3.13
0.107
3.54
SAA-FRRMEELOW
-4.51
-4.88
0,200
22.3
23.9
0.556
0.581
RIBOFLOW
-5.01
-4,82
0.304
15.4
11.2
0.522
0.656
+ PRE
-5.12
-5.07
0.469
45.7
28.1
0.561
0.537
CROP
-5.10
-4.99
0.451
27.9
12.3
0.546
0.609
PRE + CROP
-4.96
-4.84
0.460
39.8
26.1
0.559
0.540
RIBOFLOW-T
-5.07
-4.94
0.472
22.4
0.550
0.524
two protocols: (a) using the true RNA sequence length for generation, and (b) employing uniform sampling of RNA lengths within predefined range. Both protocols are tested the sequence-based evaluation set. Additionally, we expand our analysis to include the structure-bbas based and few-shot evaluation (detailed in the Appendix Table 6 and Table to assess the model's performance.
Setup. We include three types of model variants in addition the original model: (i) RiboFlow:
Table 3: Sequence recovery and structural RMSD comparisons with ground-truth RNA using ligand- bound structures and true sequence lengths on the test set.
The baseline model trained on the RiboBind; (ii)
+CROP: Model trained on the RiboBind aug- mented with dynamic cropping strategy; (iii)
+PRE: Model trained the RiboBind using codesign pretrained weights; (iv) +PRE+CROP: Model trained on RiboBind augmented with dynamic cropping strategy using codesign pre- trained weights. The ligand-bound structures are provided to models as guidance. For protocol (a). 100 RNAs of actual length are sampled for each ligan pair. As for protocol (b), the range defined [50,150], sampling inter- vals of 20, and generated 100 samples for each length. These candidate RNAs are subsequently evaluated based on their structural validity and binding capability with the ligand.
METHOD
%SR (t)
RMSD (L)
RANDOM
25.0
RNA-FRAMEFLOO
28.2
12.32
RIBOFLOW
30.9
11.66
+PRE
33.4
9.43
+CROP
31.2
10.32
+PRE+CROP
31.8
11.64
Results. We first analyze the experimental results provided with the ground-truth RNA lengths, as shown in Table 2 in the gray-shaded section and Table 3, with best results in bold and the second-best results underlined. Furthermore, we provide detailed experimental results for 24 randomly selected RNA-ligand pairs presented in Figures 12, showcasing the vina with actual values as reference. Several interesting conclusions can be summarized: (i) The diversity for all models are significantly lower than those observed in Table 1, which indicates that constraining RNA length compresses the sequence sampling space, leading to a decrease in diversity; (ii) Models incorporating co-design pre-training strategy (PRE and PRE-CROP) demonstrate substantial advantage structural validity and binding capability. Besides, we can also observe that these two models also perform excellently in terms of sequence recovery rate and RMSD metrics in Table 3;

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(iii) Compared to the codesign pretraiinn there is no significant improvement in structural validity under the dynamic cropping strategy. This may be because the trimmed RNA fragments may fold into the real RNA structure, thus the model cannot effectively learn the true structural distribution. However, data augmentation expands the training data, allowing the model learn more diverse set of RNA-ligand docking patterns and improve the designed RNA binding capability.
Subsequently, we analyze the experimental results when length is sampling from the pre-defined ranges in Table 2 highlighted in the blue-shaded section. Most experimental conclusions are similar the previous ones, but due the increase in the length sampling space, models can explore more potential binding patterns, and the results of Vina, GerNA and AFScore increase largely.
We conduct similar experiments structure- -ased evaluation and few-shot evaluation, and experi- mental results can be referred to the Appendix B.5 and Appendix B.6.
5.3 RNA Generation Guided by Ligand SMILES
A common challenge in real-world applications is the lack of knowledge regarding the precise RNA length and its ligand-bourd conformation. This section try to explore whether our model can successfully design small molecule RNA binders despite this significant limitation.
Setup. We use RDKit to generate 3D ligand conformers from SMILES, with optimization using the MMFF force field [40]. RNA lengths are sampled at intervals of 20 in the range [50, 150], with 200 structures generated per length. Moreover, we introduce RiboFlow-T, a variant that translates the coordinate system to the ligand centroid during the training of RiboFlow-PRE-CHE tion aims to minimize the impact of ligand spatial positioning on RNA-ligand complex modeling. The nucleotide structure frames are subsequently constructed relative to this transformed coordinates.
This modifica-
The experimental results are shown in Table 2 highlighted in the pink-shaded section, which reveals the following insights: RiboFlow- T achieves the highest AFScore and GerNA score, indicating that re-centering on the lig- and's centroid during training improves docking pattern reliability. However, this approach in- troduces inconsistencies with the RNA-centered coordinate system used during pretraining. re- ducing RNA structure validity. Meanwhile, we observe that when only the SMILEES is provided, the predicted RNA-binding affinity is generally higher than that obtained using the true ligand structure. We speculate that this is because the RNA molecule is designed based on the ligand structure generated from SMILES, resulting in standardized and reproducible input that hances model robustness. However, the true ligand binding conformation may deviate from the lowest-energy state due conformational con- straints within the complex. In addition, structural variations arising from different sources can introduce input heterogeneity, potentially affecting the model's generalization ability.
Results.
Ground-truhh Structure
Designed Backbons
Distance from TM-Score:0.47 %5R:41.8
Dietance from ligand
Figure 3: De novo design of RNA correspond- ing to ligand A2F Left: The ground-truit RNA- ligand complex (PDB ID: 3GOT). Right: RNA backbone designed by RiboFlow. The designed structure achieves a TM-score of 0.47 and Se- quence Recovery (SR) of 41.8% compared to the ground-truth complex.
We also leverage RiboFlow to de novo design RNA conditioned on ligand A2F and GNG, with the experimental results presented in the Figure 3 and Appendix B.8.
Conclusion
In this work, we propose RiboFlow, the first generative model designed for ligand-specific RNA generation. By leveraging RNA backbone frames, torsion angles, and sequence features via con- ditional flow matching, RiboFlow can effectively capture the conformational flexibility of RNA while improving structural validity through sequence constraints. Additionally, RiboFlow models the 3D RNA-ligand interaction to optimize RNA generation, implicitly enhancing its binding affinity.

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Extensive experiments demonstrate the ability of RiboFlow to generate RNA structures with both high validity and target-specific affinity.
Acknowledgments and Disclosure of Funding
This study has been supported by the National Natural Science Foundation of China [62041209] the Science and Technology Commission Natural Science Foundation of Shanghai the Lingang Lab Fund [LGL-8888]. of Shanghai Municipality
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Answer: [NA]
Justification: The core method development in this research does not involve LLMs any important, original, or non-standard components.
Guidelines:
The answer NA
that the method development in this research does involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy (https : h/tp://ies...//eeeeeeeeee http///rrs..c//ooeeeeeeeeeeeeeee / for what should or should be described.
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APPENDIX
Dataset
A.1 Dataset Preparation
21
A.2 Dataset Comparison
A.3 Data Augmentation
B More Results and Analysis
23
B.1
The Impact of Pre-training Steps on RNA Generation
B.2 The Local Structure of RNA Analysis
24
B.3 The Impact of Sampling Lengths on Model Generation Quality
25
B.4
Using Different Backbone Atoms for Model Evaluation
26
B.5
Experimental Results on Structure-based Evaluation
26
B.6
Experimental Results on Few-shot Evaluation
26
B.7 Sampling Time Efficiency Analysis
26
B.8 Using RiboFlow for Novo RNA Design
C More Details of RiboFlow Training and Generation
29
C.I Model Training Details
................................
C.2 Model Inference
D Flow Matching
30
Limitations and Broader Impact
33
A
Dataset
A.1
Dataset Preparation
In this work, we propose RiboBind, the largest RNA-ligand binding dataset to date. RiboBind contains all RNA-small molecule interaction information stored in the RCSB PDB database up to December 2024. The data preparation workflow is described in detail as follows.
(i) Data collection. We obtain structural data in mmCIF format from the RCSB PDB database using the advanced search query, which supports custom filtering based on specific structural attributes. The search is restricted structures containing at least one RNA polymer chain and one ligand instance. These criteria are defined using the following query settings: Structure Attributes -> Number of Distinct RNA Entities of Non-polymer Instances >= 1. This step serves as the initial data collection phase.
and Structure Attributes
Total Number
(ii) Ligand filtering. We adopt portion of the ligand selection criteria from the HairBoss database [33], while relaxing certain rigid druggability restrictions, aiming to balance ligand di- versity and effective identification. Specifically, the following rules applied during our workflow: (1) the molecular weight of the ligand must fall within the range of 100-1000 Daltons, and (2) the ligand must not be solvent molecule
metal ion. Consequently, expand the original ligand library beyond the 311 ligand classes used in HairBoss by incorporating an additional 237 ligand classes, resulting in a total of 548 unique ligand classes.
(iii) RNA-ligand interactions determination. For structures containing valid ligands, RNA- ligand interactions identified by calculating the minimum distance between any ligand atom and any RNA atom. If the minimum distance less than equal to 5 Á, the ligand is considered to interact
21

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with the RNA. Using this criterion, we extract RNA chains and their corresponding interacting ligands from the crystal structures to construct RNA-ligand complexes. Each complex is subsequently decomposed into individual RNA-ligand pairs, where each pair comprises single RNA chain and its interacting ligand.
(iv) Redundancy reduction. To reduce redundancy and improve dataset quality, we employ MM- seqs2 [36] to cluster RNA sequences at a 90% sequence identity threshold. Notably, RNA sequences within the same cluster but bound to different ligands are still treated as distinct RNA-ligand pairs.
182225 Dalabase
Conaina
Conta cal Igand?
Fisi--ggad Peterce S.D.A.
Extract RMV- contiloons
Sulkiitt 10hA kgand pairs
RiboBind Reposikory
St Btrccmms without chane
Stnudd uons witour eaid Egande
Stuckres whou sigong
Figure 4: The collection pipeline of RiboBind dataset.
Le
Length>200
..GGGG....
AUC...UGGUA..UUU
An example of RNA dynamic cropping. Based on selected nucleotide, we randomly select a cropping length within the range 30 to 200 to obtain the trimmed RNA-ligand pair for amplifying the existing data.
Figure 5:
Through this comprehensive pipeline. we construct the RiboBind dataset, offering substantially larger and more diverse collection of RNA- molecule interactions compared to existing datasets. The final RiboBind dataset includes 1,591 RNA-ligand complexes and 3,012 RNA-ligand pairs. In comparison, as of December 2024, the publicly accessible HairBoss dataset comprises only 862 RNA-ligand complexes and 1, 1,471 RNA-ligand pairs. This notable increase in both scale and diversity highlights the value of RiboBind as robust resource for RNA-small molecule interaction studies. For more detailed statistical information about RiboBind, please refer to Figure 6.
A.2 Dataset Comparison
Currently, there are also some efforts to collect and curate large-scale RNA-ligand datasets. In addition to the HariBoss dataset referred to in this article, some well-known datasets include PDBBind [30] and RNAmigos2 [8]. PDBBind is comprehensive database primarily focused on experimentally measured binding affinity data for protein-ligand complexes, though it also includes a small subset of RNA-ligand complexes. In its latest version (v:2024), it contains 234 RNA-ligand complexes. According to our inspection, all of these entries are already included in the RiboBind dataset, making PDBBind essentially a high-quality subset of RiboBind in the context of RiA-ligand interactions.
The RNAmigos2 dataset, on the other hand, was introduced alongside the recently proposed RNA structure- based virtual screening tool RNAmigos2. This dataset includes 1,740 experimentally validated RNA-ligand binding site structures. After deduplication, we verified that all of these experimental structures are also present in the RiboBind dataset. In addition to the experimental data, the RNAmigos2 dataset also contains 1.3 million synthetic affinity data points, generated via in silico molecular docking. These synthetic interactions were computed by docking approximately 800 ligands (sourced from the ChEMBL database) against the 1,740 experimentally solved RNA structures
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in all pairwise combinations. Since RiboBind currently only includes experimentally determined structural data, remains fully consistent with the experimental portion of the RNAmigos2 dataset. However, we plan to incorporate the synthetic data in future versions of RiboBind to increase the chemical diversity and overall scale of the dataset, thereby enhancing the generative performance of our models.
A.3 Data Augmentation
To prevent graphics memory overflow during experiments, the RNA length restricted the range of 30-200 nucleotides during training. However, the original RiboBind dataset contains limited number of samples within this length range, which is insufficient requirements of deep generative models. To address this challenge, we introduce data augmentation strategy called dynamic cropping, designed to fully leverage existing data resources by modifying RNA sequences longer than 200 nucleotides.
satisfy the large-scale data
In order ensure that the cropped RNA sequences retain interactions with small molecules, the cropping strategy is informed by interaction data from existing datasets. Specifically, we select RNA bases involved interactions with the small molecules as candidate bases. By calculating the total interaction counts between each base and every atom in the small molecules, we can identify the top three bases with the highest interaction frequencies as cropping centers. The cropping length is randomly sampled within the range of 30-200 nucleotides. Using the selected base as the center, the RNA sequence is cropped symmetrically, with half total length taken from both upstream and downstream of the base, forming the final RNA-ligand pair. For more detailed statistical information about the amplified RihoBind dataset, please refer to Figure 7. This approach ensures that the cropped RNA sequences retain critical interaction information for downstream tasks.
Through this dynamic cropping strategy, the number of qualified RNA-ligand pairs in the original RiboBind dataset increased from 1,061 to 4,445, significantly expanding the available training data.
300
PM
460
200
186-150
100.099
140
Lenght
(a) Distribution of RNA Length
(b) Distribution of Ligand Type
Figure 6: Statistics of the originally collected RiboBind dataset. (a) The number of RNAs within different length ranges, and the vast majority of RNAs exceed the 200-nucleotide limit; (b) The proportion of the top ten categories of ligands.
B
More Results and Analysis
B.1
The Impact of Pre-training Steps on RNA Generation
To fully demonstrate the impact of different pre-training steps on RNA generation, we comprehen- sively show the changes in model performance under various pre-training steps. We additionally intro- duced the seRMSD metric to evaluate the performance changes. scRMSD refers to self-cons coffisennitt Root Mean Squared Deviation between the generated and the predicted backbone atoms to reflect structural validity. During the pre-training stage, a batch size of 32 is selected, utilizing A100 80GB accelerator cards, completing 200K pre-training steps in nearly 20 hours. The experimental results are shown Table 4. 50K 30K_step_55 50 means that the model samples with a sampling step 50 after training for 50K steps.
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430
tip
(a) Distribution of RNA Length
(b) Distribution Ligand Type
Figure 7: Statistical analysis of the RiboBind dataset, amplified using dynamic cropping strategy after removing sequences shorter than 30 nucleotides, reveals the following: (a) The amplified dataset exhibits
balanced distribution of RNA lengths compared to the original RiboBind dataset, making better suited for model training. (b) The top ten ligand categories in the amplified dataset are still predominantly composed of the seven major ligands (FME, GTP, SAM, PHE, SPM, MPD, and SPD) found in the original dataset. This suggests that the ligand bias introduced by the amplification process remains within acceptable range.
Table 4: Detailed results of unconditional generation under different pre-training steps.
METHOD
SscTM(1)
&sCRSMD(1)
DIVERSITY(1) NOVELTY(!)
RNA-FRAMEFL.OW
STEP_50
25.0
24.7
0.573 0.503
0.582
STEP_100
30.3
28.3
0.594
RNA-FRAMEFLOW
50K_STEP_B5
14.3
16.0
0.733
0.677
50K_ _OK_STEP__ 100
20.3
19.3
0.703
0.670
100K_STEP_C_
27.7
26.7
0,550
0.597
100K_sTEP__1_
28.3
27.0
0.547
0,586
150K_STEP__6
26.3
22.7
0.577
0,562
150K_STEP_110
30.7
24.8
0.530
0.546
200K_STEP
25.0
22.3
0.590
0.553
200K SrOK_sTEP__00
27.0
24.3
0.537
0.559
RIBOFLOW
50K_STEP
14.0
21.7
0.710
0.685
20.7
23.3
0.643
0.511
100K_STEP_50
30.3
29.0
0.583
0.589
100K_STEP_100
31.0
27.3
0,620
0.574
150K_STEP_55
34.7
31.3
0.550
0.540
150K_ste 150K_sTEP___ STEP_100
31.9
30.7
0,545
0.577
200K_STEP__6
32.7
26.7
0,520
0,581
200K_STEP_ND
100
30.7
27.0
0.493
0.606
B.2 The Local Structure of RNA Analysis
To demonstrate the validity of the generated RNA structures, we conduct a detailed comparison between the RNA structures generated by RNAFrame-Flow Flow-R and RiboFlow within the [50, 150] range and the actual RNA structure distributions. We primarily illustrate histograms of the probability distributions for the bond distances between nucleotides, the bond angles between nucleotide triplets, and the torsion angles, as shown in Figure 8. It can be observed that the structures of the RNA we generated are similar distribution to those of real RNA, capable of reproducing the characteristies of these local structures.

---

Geseradion
Osceration
Geeerileo
0.13
0.125
0.100
0.975
0.45
0.000
10000 0
Ground Thuth Generation
2200
Ground Truth Generation
Ground Truth
0.175
Generation
0.4
0.150
0.20
Prbabtt
0.3
0.125
0.15
0.2
..050
0.1
0.05
0.025
0.0
0.000
0.00
OR
04 04 cherral (radian))
(Angstrom)
(odiint
Figure 8:
Comparison of probability distribution histograms for nucleotide bond distances, bond angles, and torsion angles between generated and real RNA structures. Top: ZNA-FrameFlow- Bottom: RiboFlow. RiboFlow shows a closer match to the real data distribution, particularly in bond angles, indicating improved geometric realism over FromerraeF...-RR.
Method
Riboftow
0.275
RKA FruncElow
RKA-Frammellae
0.250
0.225
ScTM
0.200
0.175
0.150
Sequence Length
Figure 9: The impact of sampling lengths on model generation quality.
B.3 The Impact of Sampling Lengths on Model Generation Quality
Due to the pre-training data distribution limitations in RNASolo (which is primarily concentrated in the range of 30-200), we set the RNA sampling range to [50, 150]. However, we are willing to explore RiboFlow's performance in designing longer RNA sequences. To this end, conducted additional experiments, performing unconditional RNA backbone design for sequences of lengths [200, 250, 300, 350, 400, 450]. Notably, attempting longer sequences on an RTX 4090 GPU (24GB memory) triggers an OOM error due to the memory requirements of RhoFold. The seTM experimental results for each length
shown in Figure 9.
Our findings indicate that for longer sequences, RNA validity declines significantly compared to the performance in the [50, 150] range reported in the paper. However, it is important to note that RhoFold explicitly states that its training data only includes nucleotide sequences of lengths 16-256, and its accuracy beyond this range has not been fully validated. Therefore, this experiment provides
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Table 5: Structure generation validity of the model under unconditional generation, evaluated using different backbone atoms (C3', C4', C5') and their average.
Mi: METHOO
SOCKMND(AVG..
SSCKMNIXCS]
SSCKMSIXCE)
SUCKMSIXCS1
SACTM(AVEL
9ECTMICS"
9a:TMMCC''
RNA-FRAMEFLOW
SAMPLE, STDP 60
27.0
27.7
27.0
27.0
26-3
27.0
26.7
26.7
SAMPLE, 800
29.3
29.7
30.3
29.7
31.6
32.0
31.3
30.0
RYA-FRAMEPLOW..
SAMPLE, BroP.
REBOFLOW
$2.3
32.7
32.3
36.7
38.3
100
only a rough assessment of the model's generalization ability, We look forward to advancements in RNA engineering, where more powerful structural prediction models will enable more complex RNA designs.
B.4 Using Different Backhone Atoms for Model Evaluation
In the paper. we used the C4' atom to evaluate model performance in experimental results. To assess the validity of generated RNA structures, we employed scTM and scRMSD metrics. The TM-score was calculated using US-align [49], which by default selects single backbone atom for structural evaluation.
Recognizing that multi-atom approach could provide a more comprehensive assessment, we expand the evaluation to include the backbone atoms available in US-align (C3', C4', C5', see https: / //zhanggroup org/US-a werg/USalipge alnigg/ for detailed information). We computed scTM and scRMSD for each individual atom and their mean values enhance evaluation robustness. Note that scRMSD) refers to the RMSD between the structure predicted by RhoFold and the structure generated by the model. We refer the settings in RNA-FramcFlow [3] and define samples with scRMSD below 4.3 Á as valid structures. We report the performance of the model using different backbone atoms under unconditional generation tasks.
The results can be found in Table 5. Please note that each column in the figure represents the validity of the structure generated by using different metries for calculation. For example, &ecRMSSD(Avg)) represents the proportion of valid structures generated by calculating scRMSD using the average metric of these three atoms C3', C'',, and C5' simultancously. It can be indicate that evaluations based on multiple atoms yield consistent performance trends, supporting more accurate reflection of the model's structural generation quality.
B.5 Experimental Results on Structure-based Evaluation
Following the experimental setup Section 5.2, we conduct similar experiments on the structure- based evaluation set, with the experimental results as shown in Table 6. Our findings show that RiboFlow and its variants still exhibit strong performance under structural-based division.
B.6 Experimental Results on Few-shot Evaluation
Following the experimental setup of Section 5.2, we conduct similar experiments on the few-shot set, with the experimental results shown in Table 7. It can be observed that despite the model being exposed to only a small amount of sample information during training, it still demonstrated excellent performance. In addition, we have also presented the detailed results of vina scores for these fifteen RNA-ligand pairs in the Figure 13, fully demonstrating the experimental performance of different model variants.
B.7 Sampling Time Efficiency Analysis
In this section, we evaluate the sampling efficiency of RiboFlow in comparison to RNA-FrameFlow, a critical factor for the practical utility generative models. This analysis measures the time required to generate RNA structures of varying lengths (approximately 50 300 nucleotides) using both 50
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Table 6: Comparison of RNA generation by structure- evaluation. The best results are high- lighted in bold, while the second- best results are underlined. The table presents two experimental results: one based fixed RNA length and ligand-bound structure, and another based on ligand- bound structrees with RNA length sampling. Colored boxes denote different experimental setups.
VINA. (L)
GERNA. (1)
%A (1)
TOPI0
MEDIAN
GENERATION GIVEN LIGAND STRUCTURE AND TRUE LENGTH
RANDOM
-2.98
-2.85
0.121
5.32
RNA-FRAMELLOW
-4.33
-4.25
0.225
23.9
RIBOFLOW
-5.07
-5.01
0.231
25.2
+ PRE
-5.32
-5.26
0.452
52.3
+ CROP
-5.17
-5.09
0.389
40.2
+ PRE CROP
-5.43
-5.39
0,439
49.7
GENERATION WITH GIVEN RNA
LENGTH AND LIGAND CONFORMATION
RANDOM
-2.32
-2.15
0.110
6,47
RNA-FRAMEFLOW
-4.35
-4.24
0.217
17.4
RIBOFLOW
-5.36
-5.21
0.245
23.1
+ PRE
-5.64
-5.49
0.437
45.3
CROP
-5.41
-5.30
0.402
33.5
+ PRE CROP
-5.56
-5.38
0.393
41.3
Table 7: Comparison of model performance on the few-shot evaluation set. The best results are highlighted in bold, while the second-best results are underlined. The table presents two experimental results: one based on fixed RNA length and ligand-bound structure, and another based on ligand- bound structures with RNA length sampling. Colored boxes denote different experimental setups.
VINA. (L)
GERNA. (1)
AF (t)
TOP10
MEDIAN
GENERATION WITH GIVEN RNA LENGTH AND LIGAND CONFORMATION
RANDOM
-2.16
-2033
0.125
7.11
RNA-FRAMEFLOW
-3.95
-3.88
0.206
16.4
RIBOFLOW
-4.63
-4.49
0.276
22.8
PRE
-4.74
-4.63
0.415
47.1
+ CROP
-4.56
-4.51
0.401
25.6
+ PRE CROP
-4.47
-4.41
0.376
49.9
GENERATION WITH GIVEN RNA
LENGTH AND LIGAND CONFORMATION
RANDOM
-2.23
-2.15
0.110
8.84
RNA-FRAMEFLOW
-4.35
-4.01
0.207
19.1
RIBOFLOW
-4.87
-4.66
0.287
24.1
+ PRE
-5.04
-4.89
0.433
48.6
+ CROP
-4.91
-4.75
0.412
32.4
PRE CROP
-4.96
-4.82
0.403
51.3
and 100 sampling steps. All experiments conducted on identical hardware platform to ensure a fair and consistent comparison of computational overhead.
Figure 10 illustrates the sampling time costs. At 50 sampling steps, RiboFlow exhibits a sampling time that is largely comparable to ANA-FrameFlow across the tested sequence lengths, with RiboFlow's 's time being slightly higher in most instances. When the number of sampling steps is increased to 100, RiboFlow incurs more noticeable increase computational time relative to RNA-FrameFlow. This difference in sampling time between the two models becomes more pronounced with increasing sequence length at 100 steps, indicating that the processing of additional conditioning information in RiboFlow contributes to steeper scaling of its sampling time, particularly for longer sequences.
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Sampling Time Cost
RNA-PrameFlow (50 steps)
RiboFlow (50 steps)
RNA-PrameFlow (100 steps)
RiboFlow (100 steps)
12
Time
10
50
100
150
200
250
300
Length(nt)
Figure 10: Sampling time comparison between RiboFlow and RNA-FrameFlow. Time in seconds is plotted against RNA sequence length (nt) for both 50 and 100 sampling steps.
Nevertheless, the sampling times for RiboFlow remain within a practical and acceptable range for typical applications.
B.8 Using RiboFlow for De Novo RNA Design
In this section, aim to validate RiboFlow's 's capability in designing de novo high-affinity RNAs for specific ligand targets. As a case study, we explore two design scenarios: one where the actual RNA length and small molecule binding structure (ligand A2F2) are provided, and another where neither the RNA length nor the binding structure (ligand GNG*) is given.
Pipeline. For ligand A2F. We employ RiboFlow generate RNA sequences of length 67, which are then docked against A2F ligand-bound conformers using AutoDock Vina. A specific RNA candidate is selected based composite score integrating Vina scores and ligand pose validity, The corresponding ground-truth RNA sequence and the designed RNA sequence are presented results.
For ligand GNG. We first leverage RiboFlow generate RNAs ranging from 50 to 150 nucleotides in length. These RNA sequences are then docked against GNG conformers, generated by RDKit, using AutoDock Vina. 90-nucleotide RNA candidate is selected based on composite score that integrates vina and ligand pose validity. To validate this candidate, gRNAde is applied to determines its corresponding sequence, and AlphaFold3 predicts the structure of the resulting RNA-GNG complex.
Case Analysis. For ligand A2F: The ground-truth RNA-ligand complex used in our study has PDB ID of 3GOT. The TM-score between our designed RNA and the native RNA is 0.47, with sequence recovery rate of 41.8%. as shown Figure 3. Below, we provide both the designed RNA sequence and the corresponding native RNA sequence for comparison:
>Ground-trui truth RNA Sequence (3GOT)
RaB.lu/acccaaaaal......CCCCCCCCC >Dsiigned RNA Sequence CGC.CAA.........ccaaa.aa.......C
For ligand GNG. The AlphaFold3-predictee complex structure exhibits TM-score of 0.41 compared RNA backbone, demonstrating the validity of the designed RNA. Fur-
to the RiboFlow-designe thermore, as illustrated in Figure 11, a high consistency is observed between the GNINA-predicted binding pocket and the Appaaolld3-prdiette for RiboFlow's potential to facilitate the design RNAs with high binding affinity.
binding region. These findings provide strong support
Pnttpa://wwz rcab. weabborgr/ggga//// Phttps:/ resb. wec.sb.orggliie wwwssboorgllgggndae Gig
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AT3 Prodicted Structure
Designed Backbons
ligand
Egand
Figure 11: De novo design of 90-nucleotide RNA corresponding to the ligand GNG with molecular weight of 267.2 Da. The RNA-ligand complex structure is predicted by AlphaFold3, whereas another generated by RiboFlow and docking using GNINA. The Alphafoldd--prediceed complex structure exhibits a TM-score of 0.41 compared to the RiboFlow-designed RNA backbone, demonstrating the validity of the designed RNA.
C More Details of RiboFlow Training and Generation
C.1 Model Training Details
RiboFlow utilizes several hyperparameters in the experiments, which are crucial for the model's training and sampling processes. Therefore, we provide some key hyperparameters to facilitate the reproduction of our experiments in Table 8. The optimal hyperparameters are indicated bold.
Table 8: Hyperparameters for the RiboFlow.
Category
Hyperparameter
Value
IPA Module
Atom embedding dimension
256
Hidden dimension
16
Number of blocks
8
Query and key points
8
Number of heads
8
Key points
12
Transformer
Number of heads
4
Number of layers
4
Torsion MLP
Input dimension Hidden dimension
256
128
Ligand Module
Number of atom type
95
Number of RBF
16
Distance range
[0.05, 6.0]
Training Schedule
Translations (training sampling)
linear / linear
Rotations (training/ / sampling)
linear / exponential
Number of sampling steps Optimizer
[50, 100)
AdamW
Learning rate
le-4
Number of GPUs Batch size
4
[4. 8, 16, 32]
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Algorithm RiboFlow: Inference
1: Input: Ligand structure M, Sampling steps T, Initial RNA structure To with length N, Trained model Up
2: Initialize: steps +- O, & O, AI + 1/7
3: Initialize: complex Co with RNA To and ligand M
4: while steps < T do
5: IitA: 2t vo(x)) t.)ttAt
r+ + It (6)
(0)
exp vo(r! t(Ca)tt
6:
7:
"tAI norm (st us(s (.))
(1)
8: Sample nucleotide type: Gt
(i)
(1)
9: t At, steps + steps +1
10: end while
11: Calculate: (1)) + v xl0)
12: Return: Final complex C1
C.2 Model Inference
We provide the pseudocode in Algorithm for RiboFlow inference to intuitively demonstrate the sampling process of the model.
D Flow Matching
Flow Matching (FM) [28] is method designed to reduce the simulation required for learning Continuous Normalizing Flows (CNFs), which a class of deep generative models that generate data by integrating ordinary differential equation (ODE) over learned vector field. In this section, will provide concise overview of the flow matching approach.
A continuous normalizing flow (-) -M- M manifold M is defined as the solution to a time-dependent vector field (x(x) E TxM, where TIM represents the tangent space of M at M:
dt (x) = (x).))))).
6o(x) =
(18)
The parameter evolves within [0, 1). and the flow transforms a simple prior density Po into the data distribution p1 via the push-forward equation Pt [dbe ppo The density of is given by:
(x())= = (o()) = po (x)))e (x))e fg dz..)
(19)
The sequence of distributions p. t [0. is referred to the probability path. While the vector field i2 that generates a specific pt is generally intractable, can be approximated efficiently by expressing the target probability path mixture of simpler conditional probability paths, pt p+ (n)) These conditional paths satisfy Po = Po(x) and p1 (x)01) z 6(x x1). The unconditional probability path ps can then be recovered the average of the conditional paths with respect the data distribution: (x) pr(2|21)P1 (z1), dz
To describe this further, let u, (2(211 TzM denote the conditional vector field generating the conditional probability path (t (x)))) Flow matching builds on the insight that the unconditional vector field 2 be learned by aligning it with the conditional vector field uy (x/221) using the
following objective:
LCFM == Ee (m (..))))))))))) (x) ur (x(x1))1))
(20)
where / ~ u(0 1)), 21 ~ (x()), ~ pe(x|x1), and || || represents the norm induced by the Riemannian metric g.
The objective can be reparameterized through the conditional flow, x; ( æ1), where d satisfies d di (xr) we (wh (2ol xz) |zr) with initial condition (o(2oo/zr) 20. This allows the conditional flow matching loss to be reformulated as:
LCrM = Er,,, (m1)) [x)) dte
(21)
Once trained, samples be generated by simulating Equation (18) using the learned vector field vt-
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HH
Figure 12: The results of vina score for the 24 RNA-ligan pairs in the sequence- based evaluation set. We additionally provide the experimental values under real conditions, indicated by a blue dashed line.
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3rd
34
SNO 6 EOTE
A
22
PHC
344
SHD ST TOYO BAOR 1013
P+C
OF WLA
2
2
AAAAAAA V
PaC
2ZC CO0N MOOK 1998
PrC
PEC
PrC
P+C
JVYY VE LODDE SHAX
C
C
3nd
KVVY LOPOE NADE RAOL
Figure 13: The results of vina for the 15 RNA-ligand pairs in the few-shot We additionally provide the experimental values under real conditions, indicated by blue dashed line.
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E Limitations and Broader Impact
RiboFlow has certain limitations, which we briefly discuss here. The model's pre-training heavily depends on RNASolo, dataset of RNA structures predominantly spanning lengths of 30-200 nucleotides. Consequently, RiboFlow's generative performance on longer RNA sequences remains constrained. We anticipate, however, that with continued advances in biotechnology. more high- quality RNA crystal structures will become available, which will help alleviate this limitation. In addition, our current evaluations of RNA-ligand binding affinities are entirely computational and have yet to validated experimentally. To address this, we are actively collaborating with experimental partners to synthesize the designed RNAs and assess their binding affinities through wet-lab experiments. We are optimistic that these future experimental results will provide stronger empirical support for our approach.
As for broader impacts, our work has many potential application areas. For example, designing fluorescent aptamers that bind to specific molecules as biosensors to detect contamination, or targeting specific biological metabolites for disease treatment. Under the premise of sound legal regulation, we believe that RiboFlow will play a significant role in RNA engineering in the future to benefit all of humanity.
33


Reviews:
[{"review_id": "mxdaZ2t90b", "reviewer": "Reviewer_N2Zg", "summary": "The 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.", "questions": "1. (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.", "limitations": "see above", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### 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", "was_revised": true, "final_justification": "I remain at weak accept since overall the paper is promising and novel, but a key area of the specificity of predictions remains  largely unaddressed.", "rebuttal": "# Weakness 1\nQ: 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.\n\n\nA: For our sequence design, we primarily referred to FlexSBDD[1] (see Equation 8) and PPFlow[2] (see Section 3.4). In their work, linear interpolation was adopted for categorical types, and their experiments demonstrated that this sampling method is reasonable, a topic which is discussed in detail in both papers. We adopted their approach in RiboFlow, and our experimental results have also confirmed that linear interpolation for nucleotide types is effective.\n\n# Weakness 2\nQ: 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\n\nA: In constructing RiboBind, we primarily followed the workflow outlined in HariBoss (Section 2.1 Construction of the database). Specifically, we first queried all structures containing at least one RNA molecule and one ligand using the RCSB GraphQL API. We then selected those structures where the ligand forms non-covalent interactions with RNA.\n\nIt is worth noting that HariBoss did not explicitly exclude complexes containing proteins. If we strictly constrain the dataset to structures without any proteins, we retrieve 1,264 RNA-ligand complex structures (based on an RCSB advanced search with the following filters: Number of Distinct RNA Entities ≥ 1, Number of Distinct Protein Entities = 0, and Total Number of Non-polymer Instances ≥ 1). This set shares about 80% overlap with the broader set of 1,591 structures we obtained using a more relaxed search criterion.\n\n# Weakness 3\nQ:  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\nA: To better understand potential ligand-level leakage, we computed the maximum Tanimoto similarity (based on Morgan fingerprints) between each validation ligand and all training ligands. Under RNA sequence-based splitting, about 65% of validation ligands have similarity ≥0.7 with training ligands; under RNA structure-based splitting, about 60% do. This is expected since our dataset is split based on RNA sequence or structure, and certain ligands co-occur with highly similar or identical RNAs.\n\nWe note that the core challenge in our task is generalizing across RNA sequence and structure diversity. The model's performance depends on capturing RNA-ligand interactions rather than memorizing specific ligand structures. Nevertheless, we acknowledge this limitation and plan to incorporate ligand scaffold-based splitting strategies in future work to better disentangle ligand and RNA generalization.\n\n\n# Weaknes 4\nQ: 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\n\nA: To assess the ligand specificity of our model, we selected two chemically distinct ligands, A2F and GTP (A2F smiles: `c1[nH]c2c(nc(nc2n1)F)N`, GTP: `c1nc2c(n1C3C(C(C(O3)COP(=O)(O)OP(=O)(O)OP(=O)(O)O)O)O)N=C(NC2=O)N`), with a Tanimoto similarity of only 0.1096. For each ligand, we used RiboFlow to generate 50 RNA sequences of length 100 nt, followed by gRNAde selection to retain 8 ligand-compatible candidates, and finally used RhoFold to predict their 3D structures. We then selected those with scTM > 0.45 for downstream analysis.\n\nTo quantify the structural variation induced by different ligands, we computed the RMSD between RNA backbones generated from A2F and those from GTP. Among all cross-ligand pairs, the minimum RMSD was 3.7 Å, and the maximum reached 7.9 Å. These values suggest that RiboFlow does not simply memorize the most likely RNA structure, but generates ligand-specific folds in response to different chemical inputs.\n\n\n\n# Weakness 5\nQ: There have been other datasets curating RNA-ligand interactions, authors should discuss these and position theirs against them.\n\n\nA: Thank you for your question. PDBBind is a comprehensive database primarily focused on experimentally measured binding affinity data for protein–ligand complexes, though it also includes a small subset of RNA–ligand complexes. In its latest version (v.2024), it contains 234 RNA–ligand complexes. According to our inspection, all of these entries are already included in the RiboBind dataset, making PDBBind essentially a high-quality subset of RiboBind in the context of RNA–ligand interactions.\n\nThe RNAmigos2 dataset, on the other hand, was introduced alongside the recently proposed RNA structure-based virtual screening tool RNAmigos2. We downloaded the dataset released with the paper, which includes 1,740 experimentally validated RNA–ligand binding site structures. After deduplication, we verified that all of these experimental structures are also present in the RiboBind dataset. In addition to the experimental data, the RNAmigos2 dataset also contains 1.3 million synthetic affinity data points, generated via in silico molecular docking. These synthetic interactions were computed by docking approximately 800 ligands (sourced from the ChEMBL database) against the 1,740 experimentally solved RNA structures in all pairwise combinations.\n\nSince RiboBind currently only includes experimentally determined structural data, it remains fully consistent with the experimental portion of the RNAmigos2 dataset. However, we plan to incorporate the synthetic data in future versions of RiboBind to increase the chemical diversity and overall scale of the dataset, thereby enhancing the generative performance of our models.\n\n\n# Question 1\n\nWe sincerely apologize for the confusion caused here, as we omitted an experimental table in the appendix that would have supported this conclusion. What we intended to convey is that increasing the number of sampling steps can improve generation quality to some extent, but this improvement saturates beyond a certain point. In our experiments, we observed that around 50 sampling steps, the model’s performance reaches this upper limit, and further increasing the number of steps does not lead to significant gains in generation quality. To clarify this, we now provide a more detailed table supporting our conclusion, using RiboFlow as an example:\n\n| STEP | %VAL | DIV   | NOV    |\n|------|------|-------|--------|\n| 10   | 5.36 | 0.229 | 0.680  |\n| 20   | 16.8 | 0.324 | 0.631  |\n| 30   | 26.4 | 0.439 | 0.598  |\n| 40   | 30.1 | 0.508 | 0.532  |\n| 50   | 37.0 | 0.550 | 0.541  |\n| 70   | 36.2 | 0.548 | 0.564  |\n| 100  | 34.3 | 0.545 | 0.577  |\n| 200  | 32.1 | 0.502 | 0.613  |\n\n\n----\nRef: \n\n[1] Zhang, Z., Wang, M., & Liu, Q. (2024). Flexsbdd: Structure-based drug design with flexible protein modeling. Advances in Neural Information Processing Systems.\n[2] Lin, H., Zhang, O., et al. (2024). PPflow: Target-aware peptide design with torsional flow matching. arXiv preprint arXiv:2405.06642."}, {"review_id": "V2L2PLCGjs", "reviewer": "Reviewer_bbFE", "summary": "In 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.", "questions": "1. 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.", "limitations": "A 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.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "RiboFlow introduces a novel SE(3)-equivariant flow-matching framework for ligand-conditioned RNA co-design alongside the RiboBind dataset, and by adding forward-folding and inverse-folding benchmarks the authors have addressed the main concern of limited evaluation; given its clear novelty, rigorous experiments, and broad applicability, I recommend acceptance.", "rebuttal": "# Question 1\nEffectively capturing the conformational flexibility of RNA is critical for accurate design. The works you mentioned, such as RNAFlow and gRNAde, represent an important direction for modeling flexibility through ensembles. Our model, RiboFlow, adopts another validated strategy. Our approach aims to precisely define a single, physically plausible conformation by explicitly modeling the 8 torsion angles of RNA. We believe this method directly addresses the fundamental physical cause of RNA flexibility, the rotational degrees of freedom of the backbone. Rather than representing multiple discrete final states (an ensemble), we choose to directly model the continuous degrees of freedom that determine these states. This idea of precisely defining biomolecular structures through local frames and torsion angles has been proven to be extremely effective in several landmark works, such as AlphaFold 2 and RNA-FrameFlow.\n\nThe ablation study you suggested, removing torsion angle information, is difficult to perform directly in our ligand-conditioned RNA design task, as it would fundamentally undermine the premise of the task itself. To evaluate the interaction between RNA and a ligand, the model must generate a precise conformation that includes key atomic coordinates. The positions of these key atoms are precisely determined by the torsion angles we predict. If the torsion angle module were removed, our model would degenerate into one that can only generate a coarse-grained backbone (e.g., only C4' atoms), making it completely unable to form a chemically complete pocket for ligand binding. Consequently, meaningful ligand-guided design or evaluation would be impossible.\n\nNevertheless, to address your core question about the importance of torsion angles, we cite the ablation analysis performed by RNA-FrameFlow on the unconditional backbone generation task. (Given the limited time, we directly cite their published results here). Their study systematically compared model performance with varying levels of atomic detail: a. Frame atoms (C3', C4', O4'); b. Frame and 3 non-frame atoms (C1', P, O3'); and c. Frame and 7 non-frame atoms (C1', P, O3', C5', OP1, OP2). Observing the experimental results reveals that capturing flexibility by precisely defining the local geometry of a single conformation is also an effective strategy.\n\n| Strategy | %VAL | DIV  | NOV   |\n|----------|------|------|-------|\n| a        | 41.0 | 0.62 | 0.54  |\n| b        | 45.0 | 0.28 | 0.79  |\n| c        | 46.7 | 0.35 | 0.85  |\n\n# Question 2\n\nThank you for raising this critical question about the model’s level of structural detail. In fact, our model generates a full-atom structure that includes the majority of heavy atoms (excluding only the base-specific atoms beyond N1/N9), with an output format similar to AlphaFold2. At each denoising step, once the geometry of the backbone is predicted, we reconstruct the full-atom coordinates of each residue based on known nucleotide chemical geometry, namely, ideal bond lengths and bond angles. This is akin to the predefined nucleotide geometry information used in AlphaFold2 and is implemented following the open-source project OpenComplex on GitHub.\n\nThis reconstruction process ensures that the nucleobases not only possess correct chemical identities but, more importantly, have physically accurate and chemically reasonable 3D orientations. This allows for precise modeling of the physicochemical environment required for interactions with ligands. As for generating fully atomic RNA models, we are also actively exploring the use of full-atom foundation models such as AlphaFold3 to design RNAs with improved ligand-binding capabilities.\n\n\n# Question 3\nI will address your questions point by point:\n\n> 1. Why is the rotation diffusion schedule linear during training but exponential during sampling?\n\n\nThis design choice is thoroughly explained in the MultiFlow paper (see Appendix I.3 “Diffusion schedule and reduced noise sampling” and I.4 “Hyperparameters”). The paper clearly states that translation and rotation vectors follow different sampling strategies. This is because rotations occur in a curved, non-Euclidean space, the SO(3) manifold, where physically plausible rotational conformations form a narrow target region. During training, the goal is to teach the model to recover the original structure from any level of noise $t$. A linear schedule ensures that the model is uniformly exposed to various noise levels across the training process. However, during sampling, an exponential schedule allocates more discrete steps in the low-noise region, allowing finer refinement of the output and thus significantly improving structural detail and fidelity in the final predictions.\n\n> 2. Whether the RNA sequence sampling schedule follows a similar or different trajectory?\n\n\nFor sequence sampling, we adopt a linear schedule, which has been explicitly stated in our anonymous codebase.\n\n> 3. How are the four variable types—translations, rotations, torsions, and nucleotide types—balanced during sampling, given that for $t \\in [0, 0.5]$ (even up to $t \\approx 0.8$), the RNA structure is far from a valid fold, yet around 80% of the sequence has already been sampled?\n\nSpecifically, translations and nucleotide types (as categorical variables) follow a linear schedule, which provides a stable and computationally efficient generation trajectory. In contrast, rotations are governed by an exponential schedule, a strategy validated by models like MultiFlow and FrameFlow, allowing for more computational steps in the later phase of sampling to finely adjust critical structural details such as bond angles. As for torsion angles, inspired by AlphaFold2, we do not model them via flow matching. Instead, at each step, we directly predict them based on the current backbone geometry, as torsion angles are highly dependent on the local structural environment.\nThese three distinct generation processes are coordinated through a shared denoising network. At each step, the network receives the current states of all variables and simultaneously predicts their updates, ensuring that all components co-evolve coherently and converge toward a physically plausible final conformation within a unified framework.\n\n\n# Limitations\nThank you for your comment, which indeed addresses the core positioning of our work. The core innovation of RiboFlow lies in addressing the challenge of generating a higher-quality RNA backbone that is more compatible with specific ligands. The goal of our work is not to create a brand-new sequence design (inverse folding) model intended to surpass existing state-of-the-art tools.\n\nTo this end, we proposed a sequence-structure co-design strategy. Here, the role of sequence information is to act as a guide for structure generation, rather than being the final design product itself. We leverage the joint constraints of sequence and structure to help the model explore a more plausible conformational space, thereby generating a superior backbone. The results in Table 1 validate this core idea, showing that the accuracy of the generated backbone is significantly improved by introducing sequence information. Based on this positioning of our model, we believe that a direct benchmark comparison against SOTA inverse folding tools on a sequence recovery task would likely not accurately evaluate the true contribution of our work. This is because it would overlook the value of our model in the critical upstream stage of optimizing and improving backbone quality.\n\nTo eliminate this potential misunderstanding and in response to your suggestion, we will add a clear statement to the discussion section of the paper. This will clarify that RiboFlow is positioned as an upstream, high-quality backbone generator, designed to work synergistically with, rather than replace, downstream dedicated inverse folding tools. We believe that such a clarification will more accurately reflect the contribution of our work and place it in the proper context within the field of RNA design."}, {"review_id": "FEkIGfKLly", "reviewer": "Reviewer_YV5K", "summary": "This 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.", "questions": "1. 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?", "limitations": "1. 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?", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\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", "was_revised": true, "final_justification": "All of my concerns have been addressed. This paper should be accepted, in my opinion.", "rebuttal": "# Weakness 1\nWe believe that RiboFlow, building upon the foundation of SOTA generative models, introduces the following innovations:\n- By incorporating a distance-aware, ligand-guided module, RiboFlow achieves ligand-conditioned RNA backbone design for the first time. This significantly fills a void in the field of RNA design and introduces a novel design task to the AI4Science community.\n- RiboFlow curates the largest currently available dataset of experimentally resolved and validated RNA-ligand complexes. This addresses the problem of data scarcity in the field, which is a significant contribution for data-driven deep learning.\n\n\n# Weakness 2\nThank you for raising this excellent question. We believe that directly replacing the RoseTTAFold2NA module in RNAFlow with AlphaFold 3, while theoretically feasible, would lead to an unacceptably low sampling efficiency in practice, making it difficult to apply to real-world engineering tasks like drug discovery. The core bottleneck lies in RNAFlow's generative paradigm: in each iteration of the sampling process, a large-scale folding network (RoseTTAFold2NA or AlphaFold 3) must be called to predict the structure, causing the total sampling time to accumulate dramatically.\n\nTo illustrate this more concretely, we conducted a test:\n- On an H100 GPU, folding a 100-nucleotide (nt) RNA with AlphaFold 3 (without computing MSA features) requires approximately 30 seconds for a single inference. Following the five sampling steps recommended in the RNAFlow paper, designing just one 100-nt RNA would take 150 seconds (30s × 5). This is precisely one of the main motivations behind our development of RiboFlow (our model).\n- RiboFlow adopts a different framework that generates structures directly in geometric space, thereby bypassing the bottleneck of repeatedly calling a large folding model in a loop. As shown in our experiments (see Figure 10), RiboFlow can generate an RNA of the same length in just 6 to 10 seconds, representing an efficiency improvement of at least 15 to 25 times compared to the aforementioned approach.\n\nTherefore, an efficient generative framework like RiboFlow is crucial for engineering tasks such as drug discovery and sensor design, which require exploring vast chemical spaces and rapidly generating thousands of candidate molecules.\n\n# Weakness 3\n\nWe will address your concerns point by point:\n- We are honored to note that many reviewers have shown great interest in our data processing methods. We apologize that the presentation of the dataset's collection and processing in the current version of the main text and appendix is not sufficiently comprehensive. In a future revision, we plan to highlight the contribution of the RiboBind dataset and add a more detailed description of this workflow to better help the community advance the design of ligand-bound RNA molecules.\n- Our model does not use a separate module to enforce this sequence-structure consistency. In fact, this consistency is achieved implicitly through our joint denoising framework. At each denoising step $t$, our model makes predictions conditioned on the joint state of both the noised sequence and structure. This means that when predicting the denoising direction for the sequence, the model must incorporate the current structural information, and vice versa. This continuous, interdependent conditioning process forces the sequence and structure to co-evolve throughout the generation trajectory, ultimately converging to a self-consistent solution.\n- Regarding Section 4.5, the ligand information is directly incorporated into the RNA generation during the sampling process. We apologize for the ambiguous description here. We will provide a detailed description of this process in the appendix in a future version.\n\n\n# Question 1\n\nEquation (1) forms the foundation of our generative model. Under the Flow Matching framework, it defines a probabilistic path by which a complete RNA structure, including both its geometry and sequence, evolves from a completely random state ($t = 0$) to its true target state ($t=1$).\n- The left-hand side, $p_t(\\mathcal{T}_t|\\mathcal{T}_1)$, represents the overall conditional probability: the probability of observing a certain “noised” state $\\mathcal{T}_t$ at intermediate time $t$, given the final ground-truth RNA structure $\\mathcal{T}_1$.\n- The right-hand product form is the key to this formulation. It originates from a core modeling simplification: we assume that, conditioned on the true structure $\\mathcal{T}_1$, the generative processes of each nucleotide are independent. Therefore, the complex joint probability of the entire RNA can be factorized into the product of three component probabilities for each nucleotide $i$: translation ($p_t(x_t^i|x_1^i)$), rotation ($p_t(r_t^i|r_1^i)$), and category ($p_t(c_t^i|c_1^i)$).\n\nThis formulation decomposes the task of designing a complete RNA into $N_t$ independent and more tractable sub-problems—each focusing on the generation of an individual nucleotide’s properties. The model only needs to learn how each nucleotide’s three basic attributes evolve from noise to their true states, without having to directly handle the complex interdependencies among all atoms. This dramatically reduces the complexity of the generative modeling task.\n\n# Question 2\n\nWe believe that RiboBind is currently the most comprehensive dataset of **experimentally resolved and validated** data in the field of RNA-ligand interaction research. In our response to reviewer N2Zg, we also compared it with two authoritative datasets, PDBbind and RNAmigos2. We have ensured that all data from PDBbind and RNAmigos2, after sequence deduplication, are included in RiboBind. Meanwhile, we noted that RNAmigos2 also provides some synthetic datasets for RNA-ligand interactions, but this portion of the data is evaluated solely through docking software, without wet-lab experimental validation.\n\n# Question 3\n\nThe dynamic cropping strategy is inspired by the spatial interface cropping strategy used in AlphaFold 3. By cropping the original structure, we create a smaller structure that contains the core interaction information between RNA and ligand. This allows the model to focus more on the pocket region that interacts with the ligand molecule, thereby enabling the model to learn the structure around the pocket more accurately and efficiently. This is very important for designing RNAs with affinity for a given ligand. Therefore, as you can see, using the cropping method often yields good results on affinity metrics. However, the cropped structure is only a part of the original structure, which impacts the plausibility of the structures generated by the model to some degree. How to better balance the ratio of original data to cropped data is also one of our current research directions.\n\n# Limitations\nThank you for your kind words. We are indeed actively exploring protein-conditioned RNA design. However, unlike small molecules, proteins are structurally more complex, which makes learning the protein-RNA interactions a more challenging task. At the same time, inspired by works such as RhoDesign, we are also attempting to use RiboFlow to design and optimize existing fluorescent aptamers. We hope that RiboFlow can serve as a useful tool to advance the development of the RNA engineering field."}]

Author rebuttal:

Meta-review: This paper introduces RiboFlow, a ligand-conditioned RNA co-design framework based on SE(3)-equivariant flow matching that jointly generates RNA sequences and structures, supported by a new RiboBind dataset of RNA–ligand complexes. The approach demonstrates strong performance compared to RNA-FrameFlow, with notable improvements in validity and binding affinity metrics.

The strengths of this work include the novelty of the ligand-conditioned co-design formulation, the dataset contribution, and thorough evaluation, while weaknesses concern limited baselines, the incremental nature of the technical innovations, and questions about how well the method captures RNA conformational flexibility relative to alternatives.

During rebuttal, the authors provided clarifications, ablations, and additional experiments on forward and inverse folding tasks, as well as a comparison with an AlphaFold3-based RNAFlow variant, showing both effectiveness and efficiency advantages for their co-design strategy. With these additions, reviewers raised their scores and made an agreement on acceptance. I recommend acceptance.
Decision: Accept (poster)