NC-Bench and NCfold: A Benchmark and Closed-Loop Framework for RNA Non-Canonical Base-Pair Prediction
Authors: Heqin Zhu
Ruifeng Li
Ao Chang
Mingqian Li
Hongyang Chen
Peng Xiong
S Kevin Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: RNA secondry structure prediction
RNA non-canonical base pair
RNA foundation model
Primary area: datasets and benchmarks
TL;DR: We introduce NC-Bench, the first benchmark for RNA non-canonical base-pair prediction, and NCfold, a novel framework integrating structural priors from RNA foundation models, establishing a systematic foundation for advancing RNA structure modeling.

Abstract: RNA secondary structure forms the basis for folding and function, with non-canonical (NC) interactions indispensable for catalysis, regulation, and molecular recognition.
Despite their importance, predicting NC base pairs remains challenging due to the absence of a standardized benchmark for systematic evaluation.
To address this, we introduce NC-Bench, the first benchmark dedicated to NC base-pair prediction. NC-Bench provides 925 curated RNA sequences with 6,708 high-quality NC annotations, fine-grained edge and orientation classification tasks, and IsoScore-based embedding evaluation, offering a rigorous foundation for systematic assessment.
Building on this, we propose NCfold, a dual-branch framework that couples sequence features with structural priors derived from RNA foundation models (RFMs) via Representative Embedding Fusion (REF) and REF-weighted self-attention.
The closed-loop design iteratively refines sequence and structure representations, alleviating data sparsity and enhancing predictive accuracy. 
Experiments on NC-Bench show that NCfold outperforms existing methods, with zero-shot and ablation studies confirming its effectiveness and underscoring the need for NC-specific benchmarks.
Together, NC-Bench and NCfold establish a systematic foundation for NC base-pair prediction, advancing our understanding of RNA structure and enabling next-generation RNA-centric applications. The datasets and codes are publicly available at https://github.com/heqin-zhu/NCBench.

Published as a conference paper at ICLR 2026
NC-BENCH AND NCFOLD: A BENCHMARK AND CLOSED-LOOP FRAMEWORK FOR RNA NON- CANONICAL BASE-PAIR PREDICTION
Heqin 12, Ruifeng Li Ao Chang Mingqian Lit, Hongyang Chen" .Pngg Xiong! 1.2.5' & S. Kevin Zhou 122.,677
'School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)
MIRACCE Center, Suzhou Institute for Advance Research, USTC
*College of Computer Science and Technology, Zhejiang University
*Zhejiang Lab
BBiomeiical Basic Research Center (BBRC) of Jiangsu Province
State Key Laboratory of Precision and Intelligent Chemistry, USTC
'Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology (zhuheqinI, ruifeng. 1il. 19.11...9..0nn. 99mamml...... com
dr h.
{xiongxp, skevinzhouJ@ustec edw.e.ttceed......... en
ABSTRACT
RNA secondary structure forms the basis for folding and function, with non- canonical (NC) interactions indispensable for catalysis, regulation, and molecular recognition. Despite their importance, predicting NC base pairs remains chal- lenging due to the absence of a standardized benchmark for systematic evalua- tion. To address this, introduce NC-Bench, the first benchmark dedicated to NC base-pair prediction. NC-Bench provides 925 curated RNA sequences with 6,708 high-quality NC annotations, fine-grained edge and orientation clas- sification tasks, and IsoScore-baat based embedding evaluation, offering a rigorous foundation for systematic assessment. Building on this, we propose NCfold. a dual-branch framework that couples sequence features with structural priors derived from RNA foundation models (RFMs) via Representative Embedding Fusion (REF) and ReF-whighted self-attention. The closed-loop design iter- atively refines sequence and structure representations, alleviating data sparsity and enhancing predictive accuracy. Experiments fold outperforms existing methods, with zero-shot and ablation studies confirm- ing its effectiveness and underscoring the need for NC-specific benchmarks. To- gether, NC-Bench and NCfold establish a systematic foundation for NC base- pair prediction, advancing our understanding of RNA structure and enabling next- generation RNA-centric applications. The datasets and codes are publicly avail- able https: //tpt::///ghht com/heqin zhu/ Chu/NCBench.
NC-Bench show that NC-
INTRODUCTION
RNA molecules play essential roles in life, not only as carriers of genetic information but also reg- ulators, catalysts, and mediators of molecular interactions through their complex folding (Caprara & Nilsen, 2000; Cooper al., 2009). The secondary structure of RNA forms the foundation for tertiary folding and interaction of other molecules, and further determines its function, which is largely dominated by base-pairing patterns (Seetin & Mathews, 2012; Zhang et al., 2025). Beyond canonical Watson-Crick (A-U and G-C) and wobble base pair (G-U), non-canonical (NC) base pairs (c.g., Hoogsteen edge) and their cis and trans orientations are widespread in natural RNAs (Mladek et al., 2009). In ribozymes, riboswitches, and long non-coding RNAs, NC base pairs frequently
"Equal contributions
1 "Correppnniing authors

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participate in tertiary interactions that are essential for their structural stability and biological fune- tion (Serganov & Patel, 2007). Thus, NC base pairs are not mere exceptions but rather critical contributors to RNA structural diversity and functional specificity (Olson et al., 2019).
Despite their importance, current computational methods (Zhao et al., 2021; Seetin & Mathews, 2012) remain limited in predicting NC base pairs, largely due to the lack of standardized bench- mark to support systematic development and evaluation. For instance, thermodynamic models (e.g., RNAstructure (Reuter & Mathews, 2010)), alignment-based methods (e.g.. TurboFold II (Tan al., 2017) and PFold (Seemann et al., 2008)), and deep bearningbbsse methods (e.g.. MXfold (Singh al., 2019). UFold (Fu et al., 2022), and BPfold Zhu et al. (2025b)) are primarily designed for canonical base pairs or simple sequence-basee bascee non-canonical base pairs, with no or minimal cov- erage of geometrically details such as pair orientation and pair edge type. The absence of such NC benchmark is itself non-trivial: annotating NC base pairs requires high-resolution 3D) RNA structures (Olson et al., 2019), which are nevertheless extremely searce, and the NC pairs are highly imbalanced in distribution across types (Stombaugh et al., 2009). Furthermore, biologically mean- ingful metrics that reflect the geometric and functional complexity of NC interactions are still lack- ing (Parisien et al., 2009). These challenges have long hindered NC pairs from being benchmarked and highlight the urgent need for a unified, fair framework to drive progress.
To bridge the gap, we present NC-Bench, the first standardized benchmark for NC base-pair pre- diction. We systematically curate a dataset of 925 high-quality RNA sequences from the Protein Data Bank (PDB) (Berman al., 2000), annotated with 6,708 NC base pairs using tool RNAVIEW (Yang et al., 2003). Following the Scontis-Westher scheme (Leontis & Westhof, 2001), we ex- tract NC base pairs and exclude canonical Watson-Crick and wobble pairs, resulting in the largest systematic collection of its kind. NC-Bench defines fine-grained classification tasks for base-pair edges (Hoogsteen, Sugar, Watson-Crick. Crick) and orientations (cis, trans). employs a standardized 4- fold cross-validation protocol and multiple metrics to ensure fair. biologically grounded evaluation, which enables fair, biologically grounded evaluation and provides a solid foundation for method de- velopment and comparison. In addition to standard accuracy- baseed metrics, NC-Bench incorporates the IsoScore metric (Rudman et al., 2021), which assesses the isotropy and information richness of RFM embeddings. Through its comprehensive design, NC-Bench establishes solid foundation for advancing RNA structure modeling and further biological applications.
Furthermore, based NC-Bench, propose NCfold, novel framework designed tackle the challenges of predicting NC base pairs, particularly the issue of data scarcity. To elaborate this challenge, leverage RFM embeddings as contextual priors, providing rich structural information beyond the small set of labeled NC pairs. Specifically, NCfold adopts a dual-branch architecture that integrates base-to-base interaction priors derived from RFMs. By ranking RFM embeddings with the IsoScore, we select the top-k most informative ones, which are then fused through Repre- sentative Embedding Fusion (REF) for downstream processing. These fused embeddings are con- verted into pairwise interaction matrices and incorporated into the model's attention mechanism via REF- ReF-weighted self-attention. This mechanism establishes a feedback loop in which sequence-level features and structural priors iteratively refine one another, enabling more accurate NC base-pair predictions. Moreover, NCfold employs multi-task learning strategy to jointly predict both edge types and orientations of base pairs, capturing the intrinsic relationship of NC pairs, thereby improv- ing performance and generalization across diverse RNA structures. To validate its effectiveness, we evaluate NCfold on NC-Bench, where it consistently outperforms traditional baselines and state- of-the-art RFMs in both edge and orientation prediction. In addition, zero-shot comparisons with methods show that NCfold achieves more accurate pairing-statte predictions, highlighting the necessity and value of NC-Bench. Together, our contributions can be summarized in three parts:
We construct NC-Bench, the first standardized benchmark targeting RNA non- canonical base-pairs, which comprises 925 RNA sequences with 6,708 curated annota- tions for fine-grained edge and orientation tasks.
We propose NCfold, an NC pair prediction framework that combines sequential features with RFMs-derived structural priors through Representative Embedding Fusion (REF) and REF- weighted self-attention.
We conduct comprehensive experiments with evaluation in five different metrics on NC-Bench, comparing 7 traditional machine learning methods and 7 RNA language mod-
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els in different settings, followed by ablation study of NCfold. Moreover, conduct zero-shot assessments on four existing RNA canonical base pair prediction methods for reference.
2 NC-BENCH: NEW BENCHMARK FOR TRAINING AND EVALUATION
2.1 DEFINITION OF NON-CANONICAL BASE PAIR
RNA base-pairing interactions highly diverse, extending far beyond the Watson-Crick pairs (A- U and G-C) and the wobble (G-U) pair that dominate helical regions. The geometric classification proposed by Leontis and Westhof (Leontis & Westhof, 2001) provides comprehensive framework to describe this diversity. In this scheme, each base presents three distinct wydrogen-bonding edges, iee, the Watson Crick (W), Hoogsteen (H), and Sugar (S; also referred to as the shallow-groove) edges and non-edge category (N,), while the relative orientation of the glycosidic bonds is classi- fied as cis, Irans, and non-pair (N,). Combining the interacting edges of the two bases with their orientation yields 18 unique geometric categories of base pairs. Following the standard convention, cis WW families (including the G-U wobble variant) are treated as canonical, whereas all other edge-orientation combinations regarded non-canonical base pairs (NC pairs).
Task Definition In NC-Bench, given an RNA sequence x (#1..... "r..,,,,r,,,..........,... the objective is to pre- dict the non-canonical base-pairing type between any nucleotide pair j). which is nucleotide- level multiclass classification task. The task consists of two subtasks: edge prediction and orienta- tion prediction. For edge prediction, we assign each nucleotide an edge label resulting in an ordered edge pair (e)) after excluding canonical cis WW pairs (including the wobble G-U), this yields eight non-canonical edge combinations (WS, HS, SS, WH, SH, HH, HW, SW). For orientation prediction, each base pair is classified as either o E {cis, trans), with no base pairs labeled as N,. Performances evaluated mainly under per-class F1-score for both subtasks, along with additional assessments of generalization to RNA families.
{Ne, W. S, H),
2.2 BENCHMARK DATASET CONSTRUCTION
Dataset Construction We first collect 5,813 experimentally resolved RNA tertiary structures from the PDB database (Berman et al., 2000) with a cutoff date of 2023-08-09. We then apply RNAVIEW (Yang et al., 2003) to extract and identify base pairs from each PDB entry, filtering out other interactions such as stacking and 3D interactions, yielding 3,107 RNA structures with an- notated base-pair interactions. To reduce sequence redundancy, we apply mmseqs (Steinegger & Söding, 2017) with parameters " "-min-seq-id 0.8", "-c 0.8", and "-cov-mode 1", resulting in 1,673 non-redundant RNA sequences. Finally, discard sequences that do not contain canccnani base pairs, constructing the PDB-NC dataset of 925 RNA sequences containing NC pairs.
As shown in Figure 2a, we conduct detailed analysis of the 6,708 non-canonical base-pair anno- tations identified across these 925 sequences. As defined by Leontis and Westhof, RNA base pairs span 1 canonical (WW) and non-canonical (HH, SS, WH, HW, SH, HS, SW, and WS) edge-pair categories along with two kinds of orientations (trans and cis). Among them, WH pairs are the most frequent, accounting for approximately 26.94% of all NC pairs, followed by SH (18.50%), ), HW (13.80%), and SW (13.19%) In contrast, the proportions of WS, HS, and SS families are each below 9. 9.00%. while HH pairs are the rarest, contributing only 3.40%. Regarding orientation, about 58.96% of NC pairs adopt the trans configuration, whereas 41.04% are cis. These statistics high- light the uneven distribution of NC pair families and orientations, reflecting the inherent biases and complexity of non-canonical interactions in natural RNA structures.
Finally, we employ 4-fold cross- cross-validation on 925 samples, first splitting them in an 8:2 ratio into 740 vvaliiitiai samples and 185 fixed test samples. The training-validation set is further divided into four folds, with each fold serving once as validation while the others form the training set. After each round, the model is evaluated both the validation fold and the fixed test set, yielding four test results whose average is reported as the final performance. The distributions of edge families and orientations are preserved, with detailed statistics in Appendix B.
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Evaluation Metrics In NC-Bench, the prediction task is formulated multi-class classification problem with a limited dataset. To ensure a fair and comprchensive evaluation under such con- straints, we employ five complementary metrics: Matthews correlation coefficient (MCC), accuracy (ACC), precision (P), recall (R), and Fl-score (F1). Together, these metrics provide a comprehen- sive view of model performance in NC-Bench, covering overall accuracy, precision-recall balance, and robustness to class imbalance in limited data settings.
2.3 BENCHMARKING PROTOCOL FOR RFM EMBEDDINGS
To enable systematic and comparable evaluation of RNA representation learning models within the NC-Bench framework, conduct unified assessment of embeddings generated by suite of state-of-the-art RNA foundation models (RFMs). Although these models demon- strate strong performance on their respective pretraining objectives, the embedding spaces they learn exhibit substantial heterogeneity in geometric structure and semantic distribution-some produce highly anisotropic representations, while others overly compress or semantic informa- tion. This distributional disparity implies that naively fusing embeddings from all models is unlikely to yield performance gains and may instead introduce noise or semantic inconsistency.
0.175
To address this issue, we adopt IsoScore (Rudman al., 2021) a quantitative metric for embedding quality. IsoScore effectively captures the degree of isotropy and uniformity of information density in the embedding space, thereby reflecting its generalization capacity and suitabil- ity for downstream tasks. Using this metric, score and rank the emheddings from the following representative RFMs (RNA-FM (Chen al., 2022), RNAErnie (Wang al., 2024), SpliceBERT (Chen et al., 2024), UTR-LM (Chu et al., (Zou al., 2024), RiNALMo- micro (Penić et al., 2025), and structRFM (Zhu et al., 2025a)). We then select only the top-k models according to their IsoScore rankings and fuse their embeddings via mean pool- ing to construct more discriminative, robust, and semantically coherent joint representation, specif- ically tailored for non-coding RNA pair prediction.
0.125
0.000
0.060
0.225
0.000
RMAA-M
UTRUM
SpicuBERT
alooRFM
RINALMs RyAEmie
AIDO RMA
2024), AIDO.RNN--50X,
Figure I: IsoScore distributions of RFMs.
3 METHOD
3.1 NCFOLD ARCHITECTURE
Non-canonical (NC) base pairs in natural RNAs are extremely sparse, making it infeasible to reliably learn their patterns from sequence-only self-attention. In other words, sequence information alone is insufficient. To address this, we propose NCfold (Figure 2c), a novel closed-loop dual-brancl framework that integrates sequence context with structural priors derived from RFMs. Specifically, NCfold selects high-quality RFM embeddings using IsoScore, converts them into pairwise interac- tion matrices as structural interaction priors, and integrates them into the attention mechanism via REF-weighted self-attention. This yiclds guide sequence attention and, in turn, refining attention scores iteratively update the priors, enabling more robust NC base-pair prediction under data scarcity and sparse conditions.
Sequence-Matrix Framework, where priors
Notation Let x = (I1...., L denote an RNA sequence of length L, where each is a nu- cleotide token from the alphabet U, with "N" denoting an unknown nucleotide. Given a set of RNA foundation models (RFMs), we denote the token-level embedding from model I as E(f E R LLxD, where D, denotes the hidden dimension of model f. To keep the efficiency of RFM embeddings, we select the top-k embeddings by ranking the models based on IsoScore Rud- man al. (2021). The selected embeddings [E(r) (M(r)) }== Lx that encode cecleotide-to-nuclettie interactions by the "outer product mean" operation. These matrix features {M Jr=1 are then stacked along the first dimension to yield unified base-pair interaction representation M stack((M(r) }/=)) R Our prediction
then converted into matrix features
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PDB
N E C
RNAVIEW
12%
NC-Bench
13.85
11M
5S 68%
dis
Trasss Orient/idge
Wattson-Crick
mmseqs2
The first RNA non-canonical base benchmark
3.4
7a14
56%
A Sugar
Hoogssezn
querry
surget
Nudeosido-wis
Edge:
SH
WM
struciasce Gio PDB
Collactin RNA ID
Extracting NC poine: odges and orientations 3,307
Renoving seqaanoe redundincy, fillering 925
Oriaraaioo: Irate
Hougstom (II) Suprrs
b
KNA sequence
Representative Eimbedding Fusion (RFFF)
d
CCAGUU
RFM,
RFM
UAGCAG
11
Vi
selection Uniformity Score
Eddge
Sequenes stretion
Matuun
NCfold transformer
Mis
Cony
BN
GELU
Orient
REF-Weighted Self-Attention
RFF-Weighted Solf-Artention
Figure 2: Overview of NC-Bench and NCfold. The NC-Bench dataset is the first dataset for predicting RNA non-canonical base pairs, including three edges: Watson-Crick (W), Hoogsteen (H), and Sugar (S), forming eight kinds of pair-edge (HH, HW, SW, SH, WS, HS, SS, and WH) except canonical pair-edge "WW", and base-to-base orientations: trans and cis. The pipeline of dataset curation. We firstly collect 5,813 RNA 3d structures from PDB database, then extracting NC pairs using RNAVIEW, successfully obtain 3,107 samples. Finally, we utilize mmseqs2 to remove sequence redundancy, and filter sequence with at least one NC-pair and length no more than 512, obtaining 925 samples. с NCfold takes representative embedding fusion (REF) features and sequence as input, and predicts the edges and orientations of NC pairs. The RFF-weighhed self-attention in NCfold transformer.
targets are given as godge Lx4 with ground-truth s E {0, 1, 2,4 and garient E LxL33 with ground-truth y yoient E {0, 1, 1, fx1, where 0 represents no base-pat -air interaction.
Representative Embedding Fusion (REF) To construct structural priors for NCfold, we intro- duce Representative Embedding Fusion (REF). The key idea that not embeddings from RFMs are equally informative: some are redundant or isotropic, while others capture richer structural patterns (Leclercq et al., 2009). Directly concatenating all embeddings would therefore introduce noise, especially under the sparse supervision of NC base pairs. Inspired by the Mixture of Experts (MoE) framework (Shazeer et al., 2017) and guided by the benchmarking results Section REF ranks RFM embeddings using IsoScore quantify embedding quality and selects the top-k most representative candidates. Each selected embedding ((r) E R I.x is then converted into pair- wise interaction matrix by applying mean operation to the outer product, yielding matrix feature (rr) mean EEr) @ B 22). The fused matrix representation obtained via stacking as M [( } R Unless otherwise stated, RFM neural network backbones remain frozen. This fused RFM embedding is then fed into the pairwise matrix branch of our bual-branch encoder and combined with the sequence branch for downstream edge and orientation prediction.
REF-Weighter Self-Attention Non-canonical base pairs extremely sparse in RNA structures, making it difficult for purely data-driven models to capture the diversity and complexity of base pairing interactions. To overcome this, introduce REF-weighted self-attention, which injects structural priors from REF matrices directly into the attention mechanism.
The key idea is to bias the attention map toward biophysically plausible interactions while still allowing sequence features to adaptively refine them. Specifically, REF matrices M are processed through convolutional layers CONV highlight local structural signals. The processed features
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then added to the raw attention scores QK /vd, ensuring that attention not only depends sequence context but is also guided by REF priors. Formally, given sequence queries Q, keys K, and values V, the REF-weighted attention is:
(QK" + +ONN(M V.
ReF-wtighted Self-Attention X) softmax
Vd
This mechanism creates feedback loop: REF priors guide attention maps, attention scores refine the matrix features across layers. As a result, sequence and structure information are integrated in a closed-loop manner, improving robustness under data sparsity.
Dual-Branch Sequence-Matrix Framework To better integrate sequence features with struc- tural priors, design a dual-branch sequence-matrix framework that enables iterative interactions across layers. In this design, sequence and matrix representations interact bidirectionally through cascaded REF-weighted self-attention blocks. Specifically, each transformer block in the cascade processes two types of inputs: the sequence feature X; E RLxd and the attention weight matrix M, |xxLxL from the previous block, where h is the number of multi-head attention that ables parallel self-attention computations across different subspaces to enhance expressive power and efficient global context modeling.
In the i-th transformer block, the sequence branch first undergoes layer normalization and linear projections to generate queries, keys, and values:
X, LN(X.), Q = X.
K; = K
V. X, W
(2)
The matrix branch then incorporates the prior attention weights M, (stacked REF F for the first block) into the current attention computation, which firstly processed through a residual convolu- tional layer CONV. The REF-weighted attention is calculated as:
Q.K. CONV(M,)
M
softmax
X M,+1 V;
Vd
Following the multi-head attention operation, the sequence feature is updated through residual con- nection, layer normalization (LN), and feed-forward network (FFN):
XMSA, = LN(XMSA. + X.), X = XMSA, + FFN (MMA..)..).
(4)
After N cascaded transformer blocks with dual-branch dataflow, the output sequence feature X and the last attention weight matrix My capture refined base pairing patterns, ensuring that sequence-level contextual features and base-to-bass base interaction mutually reinforce each other across layers, further boosting the model building both local and global structural dependencies.
3.2 LOss FUNCTION
To produce task-specific outputs, the model employs two parallel projection layers. For the sequence pathway, fully connected layer projects the final sequence embeddings edge logits Y edge BxL4 obtaining output edge Yddge Linear( (X,), which transposed as Yedge
Prediction Heads
edge E -x-xX when the loss is computed to match the input format of cross- entropy. For the matrix pathway, a 1 x convolution maps the fused interaction matrix to orientation logits Yotient E BxXIxx obtaining the output orientation map Y Verient Convixi (M)), which is permuted to TT E Bx3xxXXL when the loss is computed.
Loss Function Given ground-truth edge labels Yedge E 1,,1, BxL and orientation labels ariient E {0, 1, BxLxt, the training objective is defined as
L = Aedge CE CF(YY
( odger
Yedge)
+ Aurient CE V
orient> Yarien
(5)
where CE denotes cross-entropy applied only to valid (non-padding entries. In practice, we ap- ply class-specifie weights in both tasks ([1, 5, 20, 20] for edges [Ne. W, H, S] and [1,20, 20 for orientations [Np trans, cisl). which effectively alleviates the imbalance between frequent and rare categories. We further empirically set Aadge 1 and Xurien 1.

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4 EXPERIMENT
During our experiments, we observed that both the number of layers in the model and the choice of batch size play a critical role determining overall performance. As shown in Table NCfold yields the most favorable outcomes when the number of transformer layers is set to 4 or 6. As shown in Table 5, the model achieves its best results when trained with a batch size of suggesting that smaller batch sizes may help the model capture fine- ene-grained patterns more effectively.
4.1 EXPERIMENTAL PROTOCOL
Setup We train our NCfold according to the framework introduced in Section 3. For the sequence embedding, we consider P pre-trained RFMs, namely RNA-FM (Chen et al., 2022), RNAErnie (Wang et al., 2024), SpliceBERT (Chen et al., 2024), UTR-LM (Chu et al., 2024), AIDO.RNA 650m (Zou et al., 2024), RiNiLMo-micro (Penić et al., 2025), and structRFM (Zhu et al., 2025a). The sources are listed in Appendix C. We adopt a 4-fold cross-validation strategy for training. The loss function is CrossEntropyLoss (Mao al., 2023) with class weighting and label smoothing (5 = 0.05) to improve generalization. The model is optimized using Adam (Loshchilov & Hutter, 2019) with a learning rate of 0.0001 and weight decay for regularization. Training runs for up to 60 epochs, with early stopping (patience=10) 10) based on validation performance. Our implementation is based on PyTorch, running on Linux server with an NVIDIA GeForce RTX 4090 (24 GB).
Baseline Methods For the NC pair prediction task, categorize the employed methods into two major groups, reflecting different perspectives on modeling RNA-related interactions: 1) Tradi- tional machine learning approaches. including Random Forest (Rigatti, 2017), Gradient Boosting (Natekin & Knoll, 2013), XGBoost (Chen et al., 2015), Stochastic Gradient Descent (SGD) (Bot- tou, 2012), Logistic Regression (LaValley, 2008), k-Nearest Neighbors (KNN) (Peterson, 2009), and Multilayer Perceptron (MLP) (Taud & Mas, 2017), which serve as strong baselines. 2) RNA foundation models (RFMs), the recently developed large-scale pretrained models, including RNA- FM (Chen et al., 2022) (pretrained on 24M sequences), RNAErnie (Wang et al., 2024), SpliceBERT (Chen et al., 2024), UTR-LM (Chu et al., 2024), AIDO.RNA RNA-650M (Zou et al., 2024). RiNALMo- miero (Penić et al., 2025), and structRFM (Zhu al., 2025a), which capture rich contextual and structural representations of RNA sequences. Their ability to leverage large corpora and learn trans- ferable features makes them promising beyond conventional ML methods. To ensure faimess, adopt identical hyperparameter settings across all experiments and employ 4-fold cross-validation for evaluation the NC-Bench dataset. Further details on the baselines are provided in Appendix C.
4.2 RESULTS ANALYSIS ON NC-BENCH
Table compares the performance of various baseline models on two NC pair prediction tasks: Pair-Edge and Orientation, using 4-fold cross-validation. In the Pair-Edge Prediction task, most traditional machine learning methods (such as Random Forest, Logistic Regression) perform poorly, with their MCC close to 0 and Fl ranging from 0.093 to 0.258, indicating limited performance. should be noted that all RFMs achieve same metrics in the edge subtask, obtaining extremely low MCC, precision, recall and F1 (MCC= 0.000, 0 085,, R R= 0.281, F1 = 0.218), which is due to the failure prediction of positives. Specifically, the frozen RFM with simple linear layer fails predict the positive edges, resulting in all negatives ("no-edge").. In contrast, our NCfold series (especially NCfold top-2) outperforms all other baselines, showing a superior performance in the Pair-Edge prediction task. In the Orientation prediction task, all models show high ACC (> 0.9), which is due to the majority amount of negatives ("no- "ooo-pair)) and the larger total number (L x L for orientations, compared to I. for pair-edge). When comparing under precision, recall, and F1 metrics, our NCfold series still stand out in this task, especially NCfold (top-2), which significantly outperforms other models in key metrics like Precision (0.487), Recall (0.544), and F1 (0.486). Overall, NCfold (top- 2) achieves the best performance in both tasks, showing clear and significant advantage in NC pair prediction. The statistics of REF frequency of RFMs are visualized in Appendix D.I.
We further utilize VARNA (Darty et al., 2009) to visualize three examples (PDB ID: 1G70, ITLR, 2NCI) of reference structures and predicted structures by NCfold. The detailed NC edges (Watson- Crick, Hoogsteen, and Sugar) and their corresponding orientations (trans and cis) are displayed in Figure 3 and Appendix D.6. When comparing the prediction reference structure, NCfold
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Table 1: The mean metrics of baseline methods in two NC pair prediction tasks with 4-fold cross- validation. We use three colors of blue to denote the first, second, third best performance among naive machine learning methods and RFMs. should be noted that FI and MCC are more holistic, comprehensive metrics than others, especially in situation of imbalanced classification.
Pair-Edge
Orientation
Model
MCC
ACC
P
R
F1
MCC
ACC
P
R
F1
Random Forest
-0.020
0.285
0.232
0.269
0.168
0.002
0.976
0.369
0.377
0.372
Gradient Boosting
0.042
0.554
0.274
0.293
0.258
0.002
0.924
0.371
0.379
0.365
XGBoost
-0.008
0.439
0.246
0.265
0.221
0.000
0.979
0.376
0.385
0.380
SGD
-0.028
0.178
0.106
0.211
0.101
0.111
0.799
0.396
0.539
0.369
Logistic Regression
0.007
0.106
0.096
0.173
0.093
0.026
0.050
0.327
0.434
0.037
KNN
0.014
0.345
0.255
0.279
0.188
0,000
0.979
0.376
0,385
0,380
MLP
0.000
0.638
0.185
0.281
0.218
0.163
0.939
0.395
0.472
0.402
RNA-FM
0.000
0.638
0,185
0.281
0.218
0.000
0.984
0.352
0.359
0.355
structRFM
0.000
0.638
0.185
0.281
0.218
0.005
0.972
0.358
0.361
0.357
RNAEmie
0.000
0.638
0.185
0.281
0.218
0.024
0.948
0.357
0.372
0.354
SpliceBERT
0.000
0.638
0.185
0.281
0.218
0.003
0.972
0.350
0.358
0.352
UTR-LM
0.000
0.638
0,185
0.281
0,218
-0.001
0.983
0.352
0.358
0.355
AIDO.RNA6550X
0.000
0.638
0.185
0.281
0,218
0.003
0.963
0.358
0.359
0.355
RiNALMo
0.000
0.638
0.185
0.281
0,218
0.007
0.955
0.348
0.357
0.345
NCfold (top-1) 1)
0.211
0.596
0.375
0.386
0.341
0.285
0.966
0.474
0.524
0.482
NCfold (top-2)
0.245
0.628
0.400
0.365
0.312
0.951
0.487
0.544
0.486
NCfold (top-3)
0.219
0.603
0.370
0.376
0.336
0.265
0.948
0.463
0.520
0.466
NCfold (top-4)
0.194
0.605
0.347
0.366
0.321
0.256
0.935
0,458
0.516
0.456
1G70: Reference, NClold
1TLR: Reference, NCfold
2NC1: Relerence, NClold
Figure 3: Visualization of NC edges (Watson-Crick o/e, Hoogsteen D' and Sugar A and corre- sponding orientations (trans/cis) from the reference structures and predictions by NCfold.
predicts accurate canonical base pairs, pseudoknot (intersecting lines), and NC base pair edges and orientations. The performance can be further improved once the model is trained on more canonical and pseudoknot annotations. Moreover, NCfold tends predict more false positive pseudoknots, which can be relieved through postprocessing and adjusting the thresholds of the orientation map.
4.3 ZERO-SHOT EXPERIMENTS
Current methods for predicting the RNA secondary structure are inherently constrained to predicting only canonical base pairing status, namely "paired" and "non-pair" states, with NC pairings inferred solely from nucleotide information. Consequently, these existing methods cannot predict edge cat- egories or orientations of base pairs. To evaluate and illustrate the gap in predicting NC pairings, compare NCfold with four representative state-of-the-art RNA secondary structure prediction methods (MXFold2 (Sato et al., 2021), SPOT-RNA (Singh et al., 2019), UFold Fu et al. (2022), and BPfold (Zhu al., 2025b)) using zero-shot assessments. These assessments focus explicitly how
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well each method predicts the pairing status of NC base pairs, which is framed binary classifica- tion task involving two mutually exclusive categories: "pair" and "non-pair". To extract NC pair pre- dictions from the compared methods, exclude all canonical base pairings, namely "A-U". "G-C", and "G-U" pairs. Notably, NCfold predicts the specific edge categories and orientations of NC pairs, level of detail that is not directly comparable to the outputs of pairing status by other methods. To enable a sfference-basee comparison, we convert NCfold 's predicted orientations (i.e., "trans" and "cis") the "pair" category, aligning its output with the prediction format of existing methods. As shown in Figure 4 (Table 6), existing methods (in- cluding BPfold, UFold, MXfold2. and SPOT-RNA) are designed to predict canonical base pairing status and thus fail to effectively prediet NC pairing status. This failure results in only small numbers of positive predictions, leading to high precision but extremely low recall (approximately zero). In contrast, NCfold achieves the highest F1 score of 0.440 among all state- of-the-art art methods evaluated, as it makes both accurate positive and negative predictions, yielding a precision of 0.489 and recall of 0.431. Additionally, since the negative category (i.e.. "non "oon-pair") constitutes the ma- jority of the L x L pairing space, all methods achieve high accuracy values greater than 0.950. Given the lack of existing benchmarks for NC pairs and methods tailored to NC pair prediction, NC-Bench fills this gap by serving as the first dataset for advancing RNA structure modeling and deepening biological understanding RNA.
Acpuracy
Predsion
3495
0.463
MCC
0,4
UFold
SPOT-RNA
NCfold
score
Figure 4: Zero-shot performances on pair- ing status ("pair" and "non-pair").
4.4 ABLATION STUDY
Table 2: Results of the ablation study. The best method is highlighted in bold. (NCfold-base:
without incorporating RFMs embeddings; NCfold-BPE: with BPfold's base pair motif energy).
Pair-Edge
Orientation
Model
MCC
ACC
P
R
F1
MCC
ACC
P
R
Fl
NCfold-base
0.084
0.605
0.221
0.352
0,251
0.217
0.948
0.402
0.522
0.419
NCfold-BPE
0.211
0.656
0.358
0.402
0.335
0.326
0.966
0.435
0,562
0.464
NCfold
0.245
0.628
0.400
0.409
0.365
0.312
0.951
0.487
0.544
0.486
Table shows the results of three models: NCfold-base, NCfold-BPE, and NCfold. NCfold-base represents the model without incorporating the RFMs structural prior, while NCfold-BPE incor- porates BPfold's base-to-base energy priors. NCfold-base shows the worst performance across all metrics, with low MCC (0.084) and F1 score (0.251) in the Pair-Edge prediction task, and the low- est MCC (0.217) in the Orientation prediction task. NCfold-BPP -PE performs better than NCfold- base in both tasks, with notable improvement in MCC (0.326) and F1 (0.464) for orientation prediction. However, it still lags behind NCfold, which incorporates RFMs for embedding and achieves the best overall performance. NCfold shows the highest F1 score in both tasks, with an F1 of 0.365 in Pair-Edge. and an Fl of 0.486 in Orientation. This clearly demonstrates that incorporating RFMs into the model leads to the most significant performance improvements.
CONCLUSION AND DISCUSSION
We present NC-Bench and NCfold, the first standardized benchmark and framework for non- canonical RNA base-pair prediction. Extensive experiments show that NCfold significantly out- performs both traditional machine learning baselines and ofate-of-the-art RFMs, while ablation and zero-shot studies highlight the importance of base-to- to-base interaction priors and confirm the neces- sity of NC-Bench for advancing RNA structure modeling.

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Looking forward, envision several directions for future work. First, while NC-Bench repre- sents the largest curated dataset to date, its scale remains modest compared to canonical base-pair resources; extending the dataset with new experimental structures will further enhance benchmark robustness. Second, NCfold currently operates under a supervised learning paradigm; integrating semi-supervised or generative approaches could better exploit unlabeled RNA data. Finally, the closed-loop dual-branch design introduced here may be extended to other RNA-centric tasks, such as tertiary structure modeling, RNA-protein interaction prediction, or RNA design, thereby broad- ening the impact of this framework.
Conclusively, NC-Bench and NCfold establish systematic foundation for advancing NC pair pre- diction, contributing both a rigorous evaluation standard and a novel methodological framework that deepens our understanding of RNA structural complexity and enables the development of next- generation RNA-centric applications. More discussion is provided in Appendix E.
ACKNOWLEDGMENTS
This work is supported by National Natural Science Foundation of China (62501537 to H.C., 02271465 S.K.Z., 32370581 to P.X.). Suzhou Basic Research Program (SYG202338 to S.K.Z.), and National Key Research and Development Program of China (2033YFB4502400 to H.C.).
ETHICS STATEMENT
This paper proposes a new benchmark, NC-Bench, for NC pair prediction and introduces NCfold to address the data sparsity issue inherent in the NC-Bench dataset. To the best of knowledge, no ethical concerns arise beyond those commonly associated with research in this field.
REPRODUCIBILITY STATEMENT
We have described the model architecture, training procedure, and evaluation protocols in detail within the paper ensure reproducibilityy Upon acceptance, will make all source data and the full implementation publicly available.
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WS Published as a conference paper at ICLR 2026
A
THE USE OF LARGE LANGUAGE MODELS
large language model (LLM) was used a strictly limited manner during the preparation of this manuscript, solely to assist with minor language editing tasks such as grammar checking and formatting. All ideas, methods, analyses, and scientific interpretations were entirely developed and written by the authors without the involvement of the LLM. The authors thoroughly reviewed and, when necessary, revised any text suggested by the LLM to ensure that the final manuscript fully represents their own original work.
B NC-BENCH BENCHMARK DETAILS
B. DATASET STATISTICS
Table 3: Distribution non-canonical (NC) base-pair edges and orientations in the NC-Bench
dataset (925 RNA sequences with 6,708 annotated NC pairs).
Pair-edge family
Count
Percentage
Average per sequence
513
7.65%
0.55
HS
593
8.84%
0.64
SS
515
7.68%
0.56
WH
1,807
26.94%
1.95
SH
1,241
18.50%
1.34
HH
228
3.40%
0.25
HW
926
13.80%
1.00
SW
885
13.19%
0.96
Total NC pairs
6,708
100%
7.25
Orientation
Count
Percentage
Average per sequence
cis
2,753
41.04%
379.62
trans
3,955
58.96%
545.38
Total NC pairs
6,708
100%
7.25
Table 3 reports the distribution non-canonical base-pair edges and orientations in the NC-Bench dataset (925 RNA sequences with 6,708 annotated NC pairs). Following the scheme (Leontis Westhof, 2001), we summarize eight pair-edge families (W/H/S denote Watson- Crick, Hoogsteen, and Sugar edges, respectively) and two orientations (cis/trans). The WH family the most prevalent (26. 94%, 1.95 pairs per sequence on average), followed by SH (18.50%), HW (13.80%), and SW (13. 19%), whereas HH is rare (3.40%; 0.25 per sequence). Overall, sequences contain on average 7.25 NC pairs. For orientations, trans dominates (58.96%) over cis (41.04%) Based on counts, the correct per-sequence averages should be 2.98 (cis) and 4.28 (trans), summing to 7.25. Note that the screenshot shows 379.62 and 545.38 under Average per sequence; these values equal the percentage times the number of sequences (925) and therefore per-sequence averages. We will correct this column to avoid confusion. These statistics reveal sub- stantial class imbalance across edge families and strong preference for the trans orientation, which should be considered in model training (e.g., class weighting resampling) and in reporting both micro- and macro-averaged evaluation metrics.
not represent
B.2 EVALUATION METRICS
For performance evaluation of predicted NC edges and orientations, use MCC, ACC, P, R, and F1 to assess the quality of base pair prediction. These metrics are computed based on the con- fusion matrix of classification task, where true positive (TP) is the number of correctly predicted edges/orientations, false positive (FP) is the number of incorrectly predicted edges/orientations false negative is the number of failure predictions of edges/orientations, and true positive (TN) is correctly
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predicted non-edge/non-palt respectively. Based on these, metrics defined as follows:
TP TN FN VTP HFP IN EN FNv IN TP FN
MCC
TP IP+TN TN
ACC
TP F TP
P TP FP TP
R TP + FN
P R
Fl P+R R
C MORE DETAILS OF BASELINES
In this section, we make a detailed description baseline models that are used in our experiments, including traditional machine learning methods and RFMs.
C.I TRADITIONAL MACHINE LEARNING MODELS
We implement several classical machine learning models as baselines for our experiments. All models are configured to produce probabilistic outputs and are implemented using Scikit-learn.
Random Forest (RF): 400 trees with unlimited depth, balanced subsampling for class weighting, and parallelized computation.
Gradient Boosting: 300 trees with learning rate 0.1.
XGBoost: 300 trees with maximum depth 6, learning rate 0.1, and multi-class softmax objective.
Stochastic Gradient Descent (SGD): Logistic regression variant with log loss and maxi- mum 2000 iterations.
Logistic Regression: L2 regularization with balanced class weights and maximum 2000 iterations.
Support Vector Machine (SVM): RBF kernel with C=1.0 and probability estimates en- abled.
K-Nearest Neighbors (KNN): 15 neighbors with distance-weighted voting.
C.2 RNA FOUNDATION MODELS
As for RFMs, we construct the NC pair prediction model by simply employing the sequence em- beddings generated by each RFM, followed by learnable linear layer. The output and its inner production of this model construct the edge and orientation prediction. respectively.
D MORE EXPERIMENTS
D.1 DETAILS OF SELECTION OF RFM EMBEDDINGS
Figure summarizes the selection frequency different RNA foundation models (RFMs) during the construction of Representative Embedding Fusion (REF) under various top-k settings. When only the top-1 model selected, UTR-LM (Chu et al., 2024) dominates exclusively, being cho- sen in all 904 sequences. As the top-k increases, additional RFMs start to contribute: under top-2, RiNALMo (Penić et al., 2025) and RNA-FM (Chen et al., 2022) appear with notable frequencies (599 and 248, respectively); under top-3, four RFMs (UTR-LM, RiNALMo, RNA-FM, and stru- cRFM (Zhu et al., 2025a)) are all frequently selected, and by top-4 nearly all candidate RFMs are involved. This trend indicates that while UTR-LMM embeddings consistently strong and widely
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Top-1
Top-2
Top-3
Topu
105
300
400
unea
mound
opcctter
ADD Rout
unsus nouute
socert
unau
asouse
Commter
AnD
Figure 5: Statistics of Representative Embedding Fusion (REF) frequency of each RNA foundation model (RFM) under different settings of top-k.
preferred, integrating multiple RFMs becomes increasingly beneficial when the system is allowed select more candidates. The diverse composition of REF higher top-k settings highlights the complementary strengths among RFMs.
D.2 HYPERPARAMETER SELECTION
Table 4: Ablation study of NCfold under the different settings of transformer layers.
Pair Edge
Orientation
Layers
MCC
ACC
P
R
Fl
MCC
ACC
P
R
Fl
2
0.131
0.602
0.254
0.345
0.271
0.214
0.927
0.421
0.528
0.433
4
0.170
0.614
0.319
0.359
0.305
0.285
0.953
0.461
0.546
0.472
6
0.219
0.603
0.370
0.376
0.336
0.265
0.948
0.462
0.520
0.466
8
0.194
0.583
0.355
0.377
0.330
0.268
0.960
0.467
0.508
0.470
10
0.206
0.540
0.357
0.371
0.317
0.270
0.955
0.474
0.506
0.471
Table 5: Ablation study of NCfold under the different settings of training batch size.
Pair Edge
Orientation
Batch size
MCC
ACC
P
MCC
ACC
F1
1
0.194
0.604
0.347
0.366
0.321
0.256
0.935
0.458
0.516
0.456
4
0.207
0.637
0.370
0.381
0.338
0.358
0.975
0.457
0.551
0.480
8
0.161
0.643
0.341
0.368
0.316
0.303
0.958
0.406
0.551
0.431
16
0.149
0.618
0.337
0.325
0.291
0.239
0.952
0.400
0.523
0.415
32 0.067
0.631
0.251
0.311
0.265
0.207
0.898
0.379
0.535
0.380
64
0.061
0.666
0.273
0.335
0.296
0.212
0.872
0.379
0.530
0.381
Tables and 5 present ablation studies on the effects of transformer depth and training batch size NCfold. As shown in Table 4, increasing the number of layers from performance on Pair Edge prediction, reaching the highest MCC (0.219) and F1 (0.336) at 6 layers, while further increasing to or 10 layers leads to slight degradation, likely due to overfitting or optimization difficulty. Orientation prediction is less sensitive to depth, with relatively stable per- formance across 4-10 layers and the best F1 (0.472) at 4 layers. Table 5 shows that batch size has a stronger effeet: small- polllto-moode to-moderate batch size of 4 yields the best performance Edge: MCC 0.207, F1 0.338; Orientation: MCC 0.358, Fl 0.480), whereas larger batch sizes (> 16) noticeably reduce MCC and F1 despite slightly higher accuracy. These results suggest that NCfold
gradually improves
both tasks (Pair
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benefits from moderate model capacity and small batch training, while deeper larger-bate batch configurations harm its generalization.
D.3 ZERO-SHOP SHOT ASSESSMENT
Table 6: Zero-shot evaluation of RNA secondary structure methods for NC pairing prediction. As existing methods only predict pairing status, namely "non-pair" and "pair". We convert the predicted orientations (i.c., "trans" and "cis") of NCfold the "paired" category for reference comparison. The best results are bold.
Pairing Status
Model
MCC
ACC
R
BPfold
0.799
0.965
0.885
0.021
0.033
UFold
0.625
0.965
0.752
0.027
0.047
MXfold2
0.984
0.965
0.984
0.000
0.000
SPOT-RNA
0.573
0.965
0.757
0.022
0.039
NCfold
0.463
0.968
0.489
0.431
0.440
Table shows the zero-shot performance of several RNA secondary structure predictions on NC pairing status classification. While all baseline methods (BPFold (Zhu et al., 2025b), UFold (Fu et al., 2022), MXfold2 (Sato et al., 2021), SPOT-RNA (Singh et al., 2019)) achieve high overall accuracy 0.965), they almost never predict any paired positions, leading to extremely low recall (:0.03) and near-zero Fl scores. For example, MXfold2 attains the highest MCC (0.984) and pre- cision (0.984) but recall of 0, indicating that it predicts all residues as non-paired. In contrast, NCfold achieves substantially higher recall (0.431) and F1 (0.440), demonstrating its ability tually detect NC pairs, though with a lower MCC (0.463) due to increased false positives. These results highlight the limitations of canonical structure predictors for NC pairing and the necessity of specialized models such as NCfold.
D.4 THE AACCR-AVEEAAUE ACCURACY OF THE PER-CLASS RESULTS.
Table 7: The macro-averaged accuracy of per-class results of NCfold.
Pair-Edge
Orientation
Model
Non-edge
W
H
Non-pair
trans
cis
NCfold
0.200
0.837
0.192
0.224
0.978
0.006
0.567
As Tables demonstrated, NCfold achieves highly macro-averaged accuracy for Watson edge of 0.837 than other edge types (including Non-edge, Hoogsteen, Sugar). As for base-to-base orien- tation, due to the scarcity of no-canonical base pairs, NCfold achieves higher accuracy in Non-pair of 0.978, relatively good performance on cis orientations, and extremely low accuracy of 0.006 for trans orientation.
D.5 THE DETAILED 4-FOLD PERFORMANCE OF NCFOLD.
Table The detailed 4-fold performance of NCfold. We show the performances of different fold of NCfold, together with the variance and mean value.
Edge
0.198
0.543
0.371
0.365
0.337
0.322
0375
0,442
0.537
0.467
0.223
0.651
0.387
0.381
0.352
0.345
0,974
0.447
0.555
0.433
0.195
(1540
0347
01675
0.104 0.001
0,632
3,00
0.325
0.001
0.000
0,000
NCtold
0.193
0,611
0.379
0.570
0,540
0.547
0975
0,449
0.554
0U475
16

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Published as a conference paper at ICLR 2026
D.6 MORE VISUALIZATION RESULTS
2HEM: Reference, NClold
4PMI: Reference, NCfold
5943: NCtold
5843: Reforence
Figure 6: More visualization of NC edges (Watson-Crics o/e, Hoogsteen D. and Sugar A/A) and corresponding orientations (trans/cis) from reference structures and predictions by NCfold.
E DISCUSSION
In this paper, we introduce NC-Bench, the first dedicated benchmark for predicting non-canonical (NC) base pairs in RNA structures. NC-Bench includes 925 sequences with 6,708 annotated edges and orientations, along with Isoscore-based evaluation for RFMs, providing a comprehensive stan- dard for model assessment and development. Building upon this benchmark, we propose NCfold, waasformer-basee neural network that incorporates RFMs as priors for base-to-base interactions via biased self-attention mechanism. This design enables joint modeling sequential and structural features, thereby mitigating issues related to data scarcity. Extensive experiments demonstrate that NCfold outperforms both traditional machine learning methods and RFM-based approaches.
Despite these contributions, NC-Bench and NCfold have several limitations that suggest directions for future work. First, the current scale of NC-Bench remains limited. It could be expanded by extracting additional NC annotations from predicted RNA tertiary structures, which could serve as augmented training data. Moreover, such computationally derived annotations could be further re- fined and filtered using more detailed structural rules. Moreover, NC-Bench can further be extended with more sophisticated tertiary contacts (such as base stacking) and multi-base interactions, which challenges the method development for predicting higher-order interactions and dealing with data scarsity even rarer than non non-canonical base pairs. Second, NCfold currently predicts edges and ori- entations directly from the model outputs. Incorporating prior knowledge- constraints during post-processing could further improve results. For instance, as implemented in BPfold Zhu et al. (2025b), sharp loops isolated base pairs are not allowed in predictions. Also, future work may hance the REF-weighted self-attention processing by replacing the standard convolution with row- and column-wise attention for more global view. Third, NCfold relies on fully supervised training, which is challenging given the limited dataset size. Future work could explore few-shot learning techniques better capture the distribution NC pairs and enhance prediction performance.
17


Reviews:
[{"review_id": "TbacR58ME8", "reviewer": "Reviewer_HQzU", "summary": "The paper introduces NC-Bench, a new benchmark for predicting RNA non-canonical (NC) base pairs with 925 sequences and 6,708 curated labels covering fine-grained edge (W/H/S) and orientation (cis/trans) tasks. Building on this, it proposes NCfold, a dual-branch transformer that selects top-k RNA foundation model (RFM) embeddings via IsoScore, fuses them with Representative Embedding Fusion (REF), and injects them into the model through REF-weighted self-attention. Experiments on NC-Bench include traditional ML and RFM baselines, ablations, and a zero-shot comparison to canonical-focused secondary-structure methods.", "questions": "How do you justify that 925 sequences / 6,708 labels provide enough statistical power and representativeness for a reliable benchmark? Any sampling/power analyses?\n\nTo what extent do gains come from REF selection vs. REF-weighted attention? Could modest fine-tuning of RFMs with stronger heads close the gap?\n\nWhy do frozen RFM linear probes predict all negatives on edge classification (MCC=0)? Would balanced losses, calibrated thresholds, or shallow MLPs recover positives?\n\nDo predicted NC pairs align with known geometric/biophysical patterns across RNA families? What error modes appear most often under the observed class imbalance?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "First standardized benchmark dedicated to NC base-pair prediction with defined edge/orientation subtasks.\n\nIsoScore-based ranking plus REF and REF-weighted attention to couple sequence features with structural priors.\n\nTraditional baselines, multiple RFMs, ablations, and zero-shot references are reported.", "weaknesses": "925 sequences and heavily skewed class distributions limit learning and can inflate simple metrics\n\nAll frozen-RFM + linear baselines fail to predict positives on the edge task (MCC≈0), weakening fairness/interpretability of comparisons.\n\nCanonical-focused methods largely predict “non-pair,” yielding high accuracy but near-zero recall—making the reference comparison hard to interpret.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "K6JJwPV51g", "reviewer": "Reviewer_PB2G", "summary": "This paper has two major contributions: a benchmark for predicting non-canonical base pairing interactions from 3D sequences, and a novel model to attack the proposed task. The benchmark pulls RNA structures from the PDB databank and annotates non-canonical base pairs using the RNAVIEW software. These interactions are particularly relevant for RNA function prediction and design as they often encode for highly specific interactions with other molecules, whereas the more stable canonical basepairs tend to provide the overall scaffold for the RNA fold. The proposed model is a transformer-based architecture which combines RNA foundation model embeddings with a decoder head to predict the base pair class across an input RNA sequence.", "questions": "* Did you check that RNAVIEW is able to assign base pair geometries to chemically modified nucleotides? I know this was a problem for FR3D until recently. If not, it could introduce some significant bias to the benchmark. \n* Did you have a look at the performance broken down by base pair class? Given the sharp imbalance I think we would get a better picture of model performance by reporting the performance per class as well as in aggregate as already reported.\n* For Fig 3 and section 4, how did you obtain predictions for the canonical pairs? From reading the problem formulation, it seems that the models were only trained to predict non-canonicals.\n* For future work, I would suggest some more domain-specific evaluation functions. For example, some NC classes are geometrically more similar than others, so a wrong prediction but within a similar NC class might not be as bad, see Table 4 [1]. Likewise the location of an incorrect edge call if it is slightly shifted from the ground truth could also not be so deleterious so you could use some structure-aware evaluation functions (e.g. [2], but there are many others).\n* Do you keep multi-chain RNAs?\n\n[1] Stombaugh, Jesse, et al. \"Frequency and isostericity of RNA base pairs.\" Nucleic acids research 37.7 (2009): 2294-2312.\n[2] Agius, Phaedra, Kristin P. Bennett, and Michael Zuker. \"Comparing RNA secondary structures using a relaxed base-pair score.\" Rna 16.5 (2010): 865-878.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 4, "strengths": "This is a highly valuable contribution. Direct RNA 3D structure prediction remains challenging, with all-atom models still showing limited performance due to the limited availability of 3D structures. Several new and prominent methods are turning to working with this level of structural detail with encouraging results [1][2]. Predicting non-canonicals (often termed 2.5D) provides a lot of useful structural detail and might be a better prediction target than full 3D for the time being. The proposed model also appears to be well structured. \n\n1. Karan, Aayush, and Elena Rivas. \"All-at-once RNA folding with 3D motif prediction framed by evolutionary information.\" Nature Methods (2025): 1-13.\n2. Carvajal-Patiño, Juan G., et al. \"RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.\" Nature Communications 16.1 (2025): 1-12.", "weaknesses": "1. Source code was not made available. This can be done [anonymously](https://anonymous.4open.science/). Without the source code I cannot verify the reproducibility or soundness of the results. (This is a major contributing factor for my current score)\n2. There are no measurements of variance across the 4 splits, or across different model seeds.\n3. Two important classes of structure prediction are absent from the benchmark: non-canonical motif-based prediction (e.g. CaCoFold [1], BayesPairing2 [2], JAR3d [3] and full 3D predictions (e.g. AlphaFold3-like models, RhoFold [4])\n4. IsoScore is not defined in the paper with much detail, one has to go to the original reference and since it's an important component should be better explained in the main text.\n5. The sequence similarity threshold remains somewhat unexplored. I suggest the authors try some more and less stringent settings to better understand the generalizability of proposed models.\n\n\n\n[1] Karan, Aayush, and Elena Rivas. \"All-at-once RNA folding with 3D motif prediction framed by evolutionary information.\" Nature Methods (2025): 1-13.\n[2] Sarrazin-Gendron, Roman, et al. \"Stochastic sampling of structural contexts improves the scalability and accuracy of RNA 3d module identification.\" International Conference on Research in Computational Molecular Biology. Cham: Springer International Publishing, 2020.\n[3] Roll, James, et al. \"JAR3D Webserver: Scoring and aligning RNA loop sequences to known 3D motifs.\" Nucleic acids research 44.W1 (2016): W320-W327.\n[4] Shen, Tao, et al. \"Accurate RNA 3D structure prediction using a language model-based deep learning approach.\" Nature Methods 21.12 (2024): 2287-2298.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3SoZ6g02cf", "reviewer": "Reviewer_2dn8", "summary": "The paper presents NC-Bench, a new benchmark dataset of 925 curated RNA sequences derived from the PDB. The benchmark is specifically designed for non-canonical RNA prediction tasks. Based on this benchmark, the authors propose NCFold, a framework for non-canonical base-pair prediction. NCFold utilizes embeddings from different existing RNA foundation models that are ranked by IsoScore with subsequent fusion and a specialized attention approach. The authors evaluate NCFold on NC-Bench with comparisons against different RNA foundation model baselines and general secondary structure prediction algorithms.", "questions": "1. Which exact model (weights) did you use for RNA-FM? Which for e.g. UFold? As far as I know there are different models available, some of which perform very well on e.g. data derived from PDB (although these models also typically ignore structure similarity during training which is unfortunate).\n2. What was the rationale for using IsoScore as a score for embedding quality? Why not use some embedding metrics as e.g. described in [12] for DNA Language Models? I’m not super familiar with the current literature on RFM embeddings, but it seems that there are multiple approaches available to study the embedding quality and the cited paper for IsoScore from 2021 appears quite old when considering the recent progress in FM research.\n3. As far as I can see, the matrix input in the REF-weighted self-attention is processed with convolutions to get a more local view. Did the authors also test other architectural choices? For example, some row- and column-wise attention should also work and result in a more global view, while preserving local features as well, or?\n\n[12] Awasthi, R., Mend Mend Arachchige, G. S., & Zhu, X. (2025). Unsupervised evaluation of pre-trained DNA language model embeddings. BMC genomics, 26(1), 710.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper tackles an important problem in RNA structure prediction, namely non-coding base-pair prediction\n- To my knowledge, a dedicated benchmark for NC-pair prediction is missing\n- Including the interaction edge of nucleotides as a classification task via LW-nomenclature in the prediction tasks is a good and to my knowledge novel task.\n- The proposed REF approach and REF-weighted self-attention appear interesting.\n- NCFold seems to outperform different existing baselines on NC-Bench.", "weaknesses": "**Major**:\n1. In the Introduction, the authors support their new benchmark with the claim that previous methods are “primarily designed for canonical base-pairs” predictions. I do not agree with this statement. Already early DL methods like [1] clearly state in their Abstract:\n\n>[...] Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. [...]\n\nFollowing methods like [2-4] continue leveraging the advantage of DL methods to output an L x L matrix which allows them to include nc-pairs but also pseudoknots and even multiplets. So these methods can predict all kinds of nucleotide interactions in general. The performance on nc-interactions, however, remains far behind the performance for canonical pairs. To the best of my knowledge, even the used baseline provided in [5] outputs ct files, which allows them to predict all kinds of nucleotide interactions. The prediction quality then mainly depends on the chosen weights (see questions).\n\nHowever, I generally agree that the LW-classification is missing in all these approaches.\n\n2. I’m particularly concerned about the data set construction. The authors use mmseqs to reduce redundancy between datapoints. However, the splitting of the data into train, validation, and test is not further defined. There was a lot of work recently that showed that careful data splitting is an essential part during evaluation of learning-based methods for RNA secondary structure prediction [6-9]. In this regard, I cannot fully trust the reported results. At least two recent RNA benchmarks tackle the problem of data curation for different tasks [10, 11]. In particular, RNA3DB would be a perfect fit here, since it provides a strong split based on RNA families, is based on PDB samples, and the authors could run their exact same data processing pipeline to extract pairs.\n3. Following up on this: Why do the authors exclude all WC interactions? This strongly reduces the practical usefulness of NC-Bench and makes the evaluation look a bit like it is constructed for NCFold. I agree that this procedure increases the focus on NC pairs, and from Fig4, we clearly see that there is need to improve these predictions, but it would be much more interesting (and challenging) to improve NC-pair predictions alongside general pair predictions. There could still be an option to only evaluate NC-pairs, but there is no need to exclude other pairs from the start.\n4. For a  benchmark, the API is one of the major aspects (Is it easy to use? Can I integrate it into my research without any effort?) and so is reproducibility (How is the data processed exactly? Is this all in line with the results in the paper?). I would encourage the authors to publish the code anonymously. Today, that is really easy to do (e.g. via https://anonymous.4open.science/) and allows reviewers (and only reviewers) to check the code.\n5. Generally, I'm not convinced that the proposed integration (and ranking) of multiple embeddings from RFMs is a giant step forward from the methodological perspective, even if the ranking approach via IsoScore is interesting.\n\n**Minor**:\n1. It would be interesting to see how far pseudoknots are considered here. Using PDB samples, we typically see a lot of non-canonical interactions and these include pseudoknots and base multiplets. I think these details should be shown somewhere in the paper.\n\n[1] Singh, J., Hanson, J., Paliwal, K., & Zhou, Y. (2019). RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nature communications, 10(1), 5407.\n\n[2] Fu, L., Cao, Y., Wu, J., Peng, Q., Nie, Q., & Xie, X. (2022). UFold: fast and accurate RNA secondary structure prediction with deep learning. Nucleic acids research, 50(3), e14-e14.\n\n[3] Franke, J. K., Runge, F., & Hutter, F. Scalable Deep Learning for RNA Secondary Structure Prediction. CompBio workshop at ICML’23\n\n[4] Singh, J., Paliwal, K., Zhang, T., Singh, J., Litfin, T., & Zhou, Y. (2021). Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning. Bioinformatics, 37(17), 2589-2600. → MSA input\n\n[5] Chen, J., Hu, Z., Sun, S., Tan, Q., Wang, Y., Yu, Q., ... & Li, Y. (2022). Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions. arXiv preprint arXiv:2204.00300.\n\n[6] A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more\n\n[7] Szikszai, Marcell, et al. \"Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.\" Bioinformatics 38.16 (2022): 3892-3899.\n\n[8] Flamm, Christoph, et al. \"Caveats to deep learning approaches to RNA secondary structure prediction.\" Frontiers in Bioinformatics 2 (2022): 835422.\n\n[9] Qiu, X. (2023). Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction. PLOS Computational Biology, 19(4), e1011047.\n\n[10] Szikszai, M., Magnus, M., Sanghi, S., Kadyan, S., Bouatta, N., & Rivas, E. (2024). RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction. Journal of Molecular Biology, 436(17), 168552.\n\n[11] Runge, F., Farid, K., Franke, J. K., & Hutter, F. (2024). Rnabench: A comprehensive library for in silico rna modelling. bioRxiv, 2024-01.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "I6Z1XoiUHD", "reviewer": "Reviewer_VF23", "summary": "This paper introduces NC-Bench, a small (N=925) dataset for non-canonical (NC) RNA base pair prediction. It also proposes NCfold, a complex dual-branch transformer. NCfold uses an IsoScore heuristic to select top-k RFM embeddings, which are then fused as structural priors into the attention mechanism.", "questions": "1. What motivates the use of 4-fold instead of 5-fold cross-validation, and how sensitive are the results to this choice?\n\n2. How does NCfold perform relative to MSA-based RNA LMs (e.g., RNA-MSM or other alignment-informed models)?\n\n3. How robust is the performance of NCfold to different IsoScore thresholds or numbers of selected embeddings (top-k)?\n\n4. Could the benchmark and model be extended to capture tertiary contacts or multi-body interactions in longer RNAs?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The NC-Bench dataset is a useful, though modest, contribution to the field.\n\n2. The problem of NC pair prediction is important and challenging.\n\n3. The zero-shot analysis clearly shows that existing canonical structure predictors fail at this specific task.\n\n4. The paper is well written and clearly structured. The problem, the proposed solutions (dataset and model), and the results are all explained logically and are easy to follow.", "weaknesses": "1. The proposed architecture contains multiple novel components (IsoScore ranking, REF, dual-branch fusion), but their theoretical justification and individual impact could be analyzed more rigorously.\n\n2. Some baselines are missing, particularly against MSA-based RNA language models such as RNA-MSM, which represent strong baselines for RNA structure understanding.\n\n3. The use of 4-fold cross-validation instead of the standard 5-fold setup is not explained, and the sensitivity of performance to this choice is unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)