Predicting RNA structures and functions by artificial intelligence.

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Structural imprints in vivo decode RNA regulatory mechanisms

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Recent Advances in Aptamer Discovery and Applications

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Algorithm for optimized mRNA design improves stability and immunogenicity

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RNA secondary structure prediction using deep learning with thermodynamic integration

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Geometric deep learning of RNA structure

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Emerging Roles for Intermolecular RNA-RNA Interactions in RNP Assemblies

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RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

Singh, J., Hanson, J., Paliwal, K. K., & Zhou, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-13395-9 · Cited by 415 · see citing works

Predicting RNA splicing from DNA sequence using Pangolin

Zeng, T., & Li, Y. I. (2022) · Genome Biology · doi:10.1186/s13059-022-02664-4 · Cited by 283 · see citing works

Accurate RNA 3D structure prediction using a language model-based deep learning approach

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UFold: fast and accurate RNA secondary structure prediction with deep learning

Fu, L., Cao, Y., Wu, J., Peng, Q., Nie, Q. H., & Xie, X. S. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1074 · Cited by 269 · see citing works

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

Marouf, M., Machart, P., Bansal, V., Kilian, C., Magruder, D. S., Krebs, C. F., & Bonn, S. (2020) · Nature Communications · doi:10.1038/s41467-019-14018-z · Cited by 256 · see citing works

Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions

Chen, J., Hu, Z., Sun, S., Tan, Q., Wang, Y., Yu, Q., Zong, L., Hong, L., Xiao, J., Shen, T., et al. (2022) · doi:10.1101/2022.08.06.503062 · Cited by 247 · see citing works

cat RAPID omics v2.0 : going deeper and wider in the prediction of protein–RNA interactions

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RNA structure drives interaction with proteins

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Predicting RNA–protein binding sites and motifs through combining local and global deep convolutional neural networks

Pan, X., & Shen, H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty364 · Cited by 179 · see citing works

Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction

Tang, X., Luo, J., Shen, C., & Lai, Z. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab174 · Cited by 159 · see citing works

GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm

Wang, L., You, Z., Li, Y., Zheng, K., & Huang, Y. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007568 · Cited by 152 · see citing works

An efficient approach based on multi-sources information to predict circRNA–disease associations using deep convolutional neural network

Wang, L., You, Z., Huang, Y., Huang, D.-S., & Chan, K. C. C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz825 · Cited by 150 · see citing works

Advances and opportunities in RNA structure experimental determination and computational modeling

Zhang, J., Fei, Y., Sun, L., & Zhang, Q. C. (2022) · Nature Methods · doi:10.1038/s41592-022-01623-y · Cited by 148 · see citing works

Predicting dynamic cellular protein–RNA interactions by deep learning using in vivo RNA structures

Sun, L., Xu, K., Huang, W., Yang, Y., Li, P., Tang, L., Xiong, T., & Zhang, Q. C. (2021) · Cell Research · doi:10.1038/s41422-021-00476-y · Cited by 141 · see citing works

Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning

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RNA structure probing uncovers RNA structure-dependent biological functions

Wang, X., Liu, C., Chen, L., & Zhang, Q. C. (2021) · Nature Chemical Biology · doi:10.1038/s41589-021-00805-7 · Cited by 128 · see citing works

DeepMirTar: a deep-learning approach for predicting human miRNA targets

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Generative aptamer discovery using RaptGen

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Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder

Liu, W., Lin, H. L., Li, H., Li, P., Tang, T., Zhao, Q., & Yang, L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac104 · Cited by 121 · see citing works

Discovery of G-quadruplex-forming sequences in SARS-CoV-2

Ji, D., Juhas, M., Tsang, C. M., Kwok, C. K., Li, Y., & Zhang, Y. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa114 · Cited by 117 · see citing works

miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts

Plà, A., Zhong, X., & Rayner, S. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006185 · Cited by 114 · see citing works

Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction

Li, Y., Zhang, C.-X., Feng, C.-J., Pearce, R., Freddolino, P., & Zhang, Y. (2022) · bioRxiv · doi:10.1038/s41467-023-41303-9 · Cited by 114 · see citing works

Multiple sequence alignment-based RNA language model and its application to structural inference

Zhang, Y., Lang, M., Jiang, J., Gao, Z., Xu, F., Litfin, T., Chen, K., Singh, J., Huang, X., Song, G., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1031 · Cited by 112 · see citing works

RNA Secondary Structure Prediction By Learning Unrolled AlgorithmsPreprint

Chen, X., Li, Y., Umarov, R. K., Gao, X., & Song, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05810 · Cited by 110 · see citing works

Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning

Singh, J., Paliwal, K. K., Zhang, T., Singh, J., Litfin, T., & Zhou, Y. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab165 · Cited by 108 · see citing works

TITER: predicting translation initiation sites by deep learning

Zhang, S., Hu, H., Jiang, T., Zhang, L., & Zeng, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx247 · Cited by 106 · see citing works

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

Han, W., Cheng, Y., Chen, J., Zhong, H., Hu, Z., Chen, S., Zong, L., Hong, L., Chan, T., King, I., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac377 · Cited by 95 · see citing works

GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest

Wu, Q.-W., Xia, J., Ni, J., & Zheng, C.-H. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa391 · Cited by 89 · see citing works

RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks

Li, J., Zhu, W., Wang, J., Li, W., Gong, S., Zhang, J., & Wang, W. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006514 · Cited by 81 · see citing works

PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs

Jia, C., Bi, Y., Chen, J.-X., Leier, A., Li, F., & Song, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa522 · Cited by 81 · see citing works

Hierarchical graph attention network for miRNA-disease association prediction

Li, Z., Zhong, T., Huang, D.-S., You, Z., & Nie, R. (2022) · Molecular Therapy · doi:10.1016/j.ymthe.2022.01.041 · Cited by 81 · see citing works

MGRCDA: Metagraph Recommendation Method for Predicting CircRNA–Disease Association

Wang, L., You, Z., Huang, D.-S., & Li, J. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3090756 · Cited by 74 · see citing works

Aptamers targeting SARS-COV-2: a promising tool to fight against COVID-19

Zhang, Y., Juhas, M., & Kwok, C.-K. (2022) · Trends in Biotechnology · doi:10.1016/j.tibtech.2022.07.012 · Cited by 70 · see citing works

Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement

Xiong, P., Wu, R., Zhan, J., & Zhou, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-23100-4 · Cited by 69 · see citing works

AptaNet as a deep learning approach for aptamer–protein interaction prediction

Emami, N., & Ferdousi, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-85629-0 · Cited by 68 · see citing works

PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA–lncRNA interaction prediction

Kang, Q., Meng, J., Cui, J., Luan, Y., & Chen, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa074 · Cited by 68 · see citing works

DMFLDA: A Deep Learning Framework for Predicting lncRNA–Disease Associations

Zeng, M., Lu, C., Fei, Z., Wu, F., Li, Y., Wang, J., & Li, M. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2983958 · Cited by 67 · see citing works

UNI-RNA: UNIVERSAL PRE-TRAINED MODELS REVOLUTIONIZE RNA RESEARCHPreprint

Wang, X., Gu, R., Chen, Z., Li, Y., Ji, X.-H., Ke, G., & Wen, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.11.548588 · Cited by 60 · see citing works

Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNAPreprint

Baek, M., McHugh, R., Anishchenko, I. V., Baker, D. A., & DiMaio, F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.09.507333 · Cited by 60 · see citing works

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning

Wei, H., Xu, Y., & Liu, B. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa058 · Cited by 52 · see citing works

iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network

Wei, H., Liao, Q., & Liu, B. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2964221 · Cited by 51 · see citing works

Machine learning for RNA 2D structure prediction benchmarked on experimental data

Marek, J. Ł., Antczak, M., & Szachniuk, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad153 · Cited by 50 · see citing works

Solving the RNA design problem with reinforcement learning

Eastman, P. K., Shi, J., Ramsundar, B., & Pande, V. S. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006176 · Cited by 48 · see citing works

iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network

Hou, J., Wei, H., & Liu, B. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010671 · Cited by 48 · see citing works

NMCMDA: neural multicategory MiRNA–disease association prediction

Wang, J., Li, J., Yue, K., Wang, L., Ma, Y., & Li, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab074 · Cited by 48 · see citing works

RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis

Zhang, T., Singh, J., Litfin, T., Zhan, J., Paliwal, K. K., & Zhou, Y. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab391 · Cited by 45 · see citing works

A deep learning approach to identify gene targets of a therapeutic for human splicing disorders

Gao, D., Morini, E., Salani, M., Krauson, A. J., Chekuri, A. K., Sharma, N., Ragavendran, A., Erdin, S., Logan, E. M., Li, W., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23663-2 · Cited by 45 · see citing works

PDMDA: predicting deep-level miRNA–disease associations with graph neural networks and sequence features

Yan, C., Duan, G., Li, N., Zhang, L., Wu, F., & Wang, J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac077 · Cited by 43 · see citing works

RNA inter-nucleotide 3D closeness prediction by deep residual neural networks

Sun, S., Wang, W., Peng, Z., & Yang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa932 · Cited by 39 · see citing works

RNA secondary structure prediction with convolutional neural networks

Booy, M. S., Ilin, A., & Orponen, P. (2022) · BMC Bioinformatics · doi:10.1186/s12859-021-04540-7 · Cited by 39 · see citing works

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning

Li, P., & Liu, Z. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac078 · Cited by 38 · see citing works

DeepEdit: single-molecule detection and phasing of A-to-I RNA editing events using nanopore direct RNA sequencing

Chen, L., Ou, L. S., Jing, X., Kong, Y., Xie, B., Zhang, N., Shi, H., Qin, H., Li, X., & Hao, P. (2023) · Genome biology · doi:10.1186/s13059-023-02921-0 · Cited by 38 · see citing works

Identification of piRNA disease associations using deep learning

Ali, S. D., Tayara, H., & Chong, K. T. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.02.026 · Cited by 37 · see citing works

<scp>RNA</scp> tertiary structure modeling with <scp>BRiQ</scp> potential in <scp>CASP15</scp>

Chen, K., Zhou, Y., Wang, S., & Xiong, P. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26574 · Cited by 37 · see citing works

Nucleic Acid Architectonics for pH-Responsive DNA Systems and Devices

Mattath, M. N., Ghosh, D., Pratihar, S., Shi, S., & Govindaraju, T. (2022) · ACS Omega · doi:10.1021/acsomega.1c06464 · Cited by 35 · see citing works

Assessment of three-dimensional RNA structure prediction in CASP15

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Multi-channel graph attention autoencoders for disease-related lncRNAs prediction

Sheng, N., Huang, L., Wang, Y., Zhao, J., Xuan, P., Gao, L., & Cao, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab604 · Cited by 29 · see citing works

iCircDA-LTR: identification of circRNA–disease associations based on Learning to Rank

Wei, H., Xu, Y. D., & Liu, B. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab334 · Cited by 27 · see citing works

PPAI: a web server for predicting protein-aptamer interactions

Li, J., Ma, X., Li, X., & Gu, J. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03574-7 · Cited by 26 · see citing works

LncRNA-disease association identification using graph auto-encoder and learning to rank

Liang, Q., Zhang, W., Wu, H., & Liu, B. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac539 · Cited by 26 · see citing works

Alternate RNA StructuresPreprint

Wu, M. T.-P., & D’Souza, V. M. (2020) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a032425 · Cited by 26 · see citing works

Length-Dependent Deep Learning Model for RNA Secondary Structure Prediction

Mao, K., Wang, J., & Xiao, Y. (2022) · Molecules · doi:10.3390/molecules27031030 · Cited by 25 · see citing works

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling

Singh, J., Paliwal, K. K., Litfin, T., Singh, J., & Zhou, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac421 · Cited by 25 · see citing works

Temperature effects on RNA polymerase initiation kinetics reveal which open complex initiates and that bubble collapse is stepwise

Plaskon, D. M., Henderson, K. L., Felth, L. C., Molzahn, C. M., Evensen, C., Dyke, S., Shkel, I. A., & Record, M. T. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021941118 · Cited by 25 · see citing works

iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank

Zhang, W., Hou, J., & Liu, B. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010404 · Cited by 18 · see citing works

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

Meng, X., Shang, J., Ge, D., Yang, Y., Zhang, T., & Liu, J. (2023) · BMC Genomics · doi:10.1186/s12864-023-09380-8 · Cited by 18 · see citing works

Proteomic and Transcriptome Profiling of G-Quadruplex Aptamers Developed for Cell Internalization

Zhang, Y., Wu, Y., Zheng, H., Xi, H., Ye, T., Chan, C., & Kwok, C. K. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.0c04862 · Cited by 16 · see citing works

RNA independent fragment partition method based on deep learning for RNA secondary structure prediction

Zhao, Q., Mao, Q., Zhao, Z., Yuan, W., He, Q., Sun, Q., Yao, Y., & Fan, X. (2023) · Scientific Reports · doi:10.1038/s41598-023-30124-x · Cited by 14 · see citing works

GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction

Yang, E., Zhang, H., Zang, Z., Zhou, Z., Wang, S., Liu, Z., & Liu, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107246 · Cited by 14 · see citing works

Analysis of RNA translation with a deep learning architecture provides new insight into translation control

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Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with Pseudoknots

Akiyama, M., Sakakibara, Y., & Sato, K. (2022) · Genes · doi:10.3390/genes13112155 · Cited by 10 · see citing works

BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism

Shen, C., Chen, Y., Xiao, F., Yang, T., Wang, X., Chen, S., Tang, J., & Liao, Z. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107765 · Cited by 10 · see citing works

Scalable Deep Learning for RNA Secondary Structure PredictionPreprint

Franke, J. K. H., Runge, F., & Hutter, F. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.10073 · Cited by 9 · see citing works

Physics-aware Graph Neural Network for Accurate RNA 3D Structure PredictionPreprint

Zhang, S., Liu, Y., & Xie, L. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.16392 · Cited by 6 · see citing works

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentialsPreprint

Gong, T., Ju, F., & Bu, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.19.508461 · Cited by 4 · see citing works

Improved RNA homology detection and alignment by automatic iterative search in an expanded databasePreprint

Singh, J., Paliwal, K. K., Singh, J., Litfin, T., & Zhou, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.03.510702 · Cited by 4 · see citing works

Deep learning identifies A-to-I RNA edits using nanopore sequencing data

(2022) · Nature Methods · doi:10.1038/s41592-022-01514-2 · Cited by 2 · see citing works

Accurate prediction of RNA translation with a deep learning architecture

Fan, X. (2023) · Data Archiving and Networked Services (DANS) · doi:10.17632/rsssc3hnc4 · Cited by 2 · see citing works

Machine learning in RNA structure prediction: Advances and challenges

Zhang, S., Li, J., & Chen, S. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.01.026 · Cited by 33 · see citing works

Accelerating drug discovery, development, and clinical trials by artificial intelligence

Zhang, Y., Mastouri, M., & Zhang, Y. (2024) · Med · doi:10.1016/j.medj.2024.07.026 · Cited by 32 · see citing works

Targeting RNA with small molecules, from RNA structures to precision medicines: IUPHAR review: 40

Tong, Y., Childs‐Disney, J. L., & Disney, M. D. (2024) · British Journal of Pharmacology · doi:10.1111/bph.17308 · Cited by 25 · see citing works

Autonomous laboratories in China: an embodied intelligence-driven platform to accelerate chemical discovery

Li, J., Ding, C., Liu, D., Chen, L., & Jiang, J. (2025) · Digital Discovery · doi:10.1039/d5dd00072f · Cited by 24 · see citing works

Artificial intelligence-guided strategies for next-generation biological sequence design

Zhang, P., Wei, L., Li, J., & Wang, X. (2024) · National Science Review · doi:10.1093/nsr/nwae343 · Cited by 20 · see citing works

Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions

Olawade, D. B., Kade, A., Egbon, E., Usman, S. O., Fapohunda, O., Ijiwade, J. O., & Ogbonna, C. E. (2025) · Molecular Genetics and Genomics · doi:10.1007/s00438-025-02314-x · Cited by 18 · see citing works

mRNA Secondary Structure Prediction Using Utility-Scale Quantum Computers

Alevras, D., Metkar, M., Yamamoto, T., Kumar, V., Friedhoff, T., Park, J.-E., Takeori, M., LaDue, M., Davis, W., & Galda, A. (2024) · 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00064 · Cited by 14 · see citing works

Single-round evolution of RNA aptamers with GRAPE-LM

Zhang, J., Ju, Z., Tang, S., Liu, C., Cai, Y., Zeng, H., Meng, X., Liu, B., Zhang, Y., & Wang, Y. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03007-5 · Cited by 12 · see citing works

lociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures

Tarafder, S., & Bhattacharya, D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01621 · Cited by 11 · see citing works

Biomolecular Interaction Prediction: The Era of AI

Wang, H., Meng, X., & Zhang, Y. (2025) · Advanced Science · doi:10.1002/advs.202509501 · Cited by 10 · see citing works

AI-powered microscopy image analysis for parasitology: integrating human expertise

Feng, R., Li, S., & Zhang, Y. (2024) · Trends in Parasitology · doi:10.1016/j.pt.2024.05.005 · Cited by 10 · see citing works

Enhancing RNA 3D Structure Prediction in CASP16 : Integrating Physics‐Based Modeling With Machine Learning for Improved Predictions

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Harnessing RNA therapeutics: novel approaches and emerging strategies for cardiovascular disease management

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Advances in Protein-RNA aptamer recognition and modeling: Current trends and future perspectives

Qiu, L., & Zou, X. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103133 · Cited by 6 · see citing works

Towards clinical application: Emerging strategies for ultrasensitive and selective miRNA detection

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Harnessing RNA molecules for therapy: advances in design, delivery, and clinical development

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ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures

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A Comparative Review of Deep Learning Methods for RNA Tertiary Structure PredictionPreprint

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Artificial intelligence and machine learning heuristics for discovery of ncRNAs

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Computation-Enabled Structure-Based Discovery of Potent Binders for Small-Molecule Aptamers

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COLOR: A Compositional Linear Operation-Based Representation of Protein Sequences for Identification of Monomer Contributions to Properties

Pandey, A., Chen, W., & Keten, S. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00205 · Cited by 3 · see citing works

Temperature-sensing riboceptors

Anbalagan, S. (2024) · RNA Biology · doi:10.1080/15476286.2024.2379118 · Cited by 3 · see citing works

Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts

Qiu, X. (2024) · Biology Methods and Protocols · doi:10.1093/biomethods/bpae097 · Cited by 3 · see citing works

On the Completeness of Existing RNA Fragment Structures

Hong, X., Zhan, J., & Zhou, Y. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf127 · Cited by 3 · see citing works

PAIR: protein-aptamer interaction prediction based on language models and contrastive learning framework

Zhang, J., Yan, Z., Zeng, H., & Zhu, Z. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822859 · Cited by 2 · see citing works

RNA-MobiSeq: Deep mutational scanning and mobility-based selection for RNA structure inferencePreprint

Tang, J., Shi, Y.-Z., Zhang, Z., Luo, D., Zhan, J., & Zhou, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.21.581340 · Cited by 2 · see citing works

Detecting and quantifying overparametrization in RNA language models with REDIALPreprint

Teng, D., Qiu, Y.-R., Sakthivel, G., Aranganathan, A., Herron, L., & Tiwary, P. (2026) · bioRxiv · doi:10.64898/2026.05.11.724344 · Cited by 2 · see citing works

Progress in protein pre-training models integrating structural knowledge

Tang, T.-Y., Xiong, Y., Zhang, R.-G., Zhang, J., Li, W., Wang, J., & Wang, W. (2024) · Acta Physica Sinica · doi:10.7498/aps.73.20240811 · Cited by 2 · see citing works

Influence of contact map topology on RNA structure prediction

Faber, C., Upadhyay, U., Taubert, O., & Schug, A. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1370 · Cited by 1 · see citing works

Learning RNA language through pre-trained models: From sequence to structure

Zhang, S., & Chen, S.-J. (2025) · Biophysical Reviews · doi:10.1063/5.0273433 · Cited by 1 · see citing works

Generative AI powered by nucleic acid language model enables one-round evolution of RNA aptamers

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Bridging technical innovation and computational advances in studies of RNA–protein assemblies

Ducoli, L., Srinivasan, S., Amjadi, E., & Khavari, P. A. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00931-9 · Cited by 3 · see citing works

TriRNASP: A knowledge-based potential with three-body effects for accurate RNA structure evaluation

Yuan, T., Lou, E., Zhou, Z., Tan, Y.-L., & Tan, Z.-J. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.04.003 · Cited by 1 · see citing works

Data-intelligent-driven exploration of robotic chemist systems

Jincheng, X., Chen, L., & Jiang, J. (2025) · Scientia Sinica Chimica · doi:10.1360/ssc-2025-0093 · Cited by 1 · see citing works

Towards secondary structure prediction of longer mRNA sequences using a quantum-centric optimization scheme

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Structure-Based Prediction of lncRNA–Protein Interactions by Deep Learning

Li, P., & Liu, Z. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4290-0_16

Bioinformatic database, artificial intelligence and machine learning-based tools for non-coding RNA study

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GARN3: A coarse-grained helix centered technique for RNA 3D structures prediction

Silva, J., Cohen, J., & Cordeiro, D. D. A. (2026) · PLoS ONE · doi:10.1371/journal.pone.0328609

Overcoming translational barriers in RNA–protein docking: enhancing computational accuracy for targeted drug discovery

Akram, H., Rahman, M. U., Mazhar, S., Qamer, F., & Yousaf, A. (2025) · Future Medicinal Chemistry · doi:10.1080/17568919.2025.2533061

AI/ML Approach for Predictive Modeling of RNA Structures and Chemical Reactivity Profiles

Sarabu, J., & Zaidi, S. (2025) · IEEE Integrated STEM Education Conference (ISEC) · doi:10.1109/isec64801.2025.11147372

Opportunities for RNA sequencing in physiology: from big data to understanding homeostasis and heterogeneity

Prokop, J. W., Bilinovich, S. M., Tokarski, E., Vishweswaraiah, S., Vanderweele, S., Das, A. S., Chhetri, S. B., Dao, A., Arora, S., Goodyke, A., et al. (2025) · Function · doi:10.1152/function.019.2025

Therapeutic rewiring of ceRNA networks: a computational pipeline for drug repurposing in acute kidney injury via circRNA–miRNA–mRNA axis disruption

Wang, M., & Gao, F. (2026) · Human Genomics · doi:10.1186/s40246-026-00930-1

Engineering cis- and trans-acting RNA regulators for next-generation prokaryotic synthetic biology

Tong, S., & Tong, Y. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108882

Unlocking Programmable and Creative RNA Sequence Design with RDiffusionPreprint

Wang, J., Dong, J., Yang, L., Li, T., yin, J., Chen, J., Dong, Y., Li, J., & Tan, C. (2026) · Research Square · doi:10.64898/2026.06.13.732023

RNALig: An ML‐Driven Structure‐Based Scoring Function for Estimating Binding Affinities of RNA‐Ligand Complexes

Sharma, P., Latha, N., Aggarwal, L., & Pant, P. (2026) · ChemPhysChem · doi:10.1002/cphc.70463

Predicting CRISPR-Cas13d On-Target Efficacy via Deep Learning

Liu, Z., Jiang, J., Zhang, H., Jiang, R., Niu, J., Liu, X., Zhao, T., & Chen, H. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3716-6_23

From genome editing to chromosome engineering in plants

Lei, Y., Wu, J., & Gao, C. (2026) · Trends in biotechnology · doi:10.1016/j.tibtech.2026.09.024

Deep learning for RNA structure prediction

Wang, J., Fan, Y., Hong, L., Hu, Z., & Li, Y. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102991 · Cited by 24 · see citing works

AI meets RNA: revolutionizing structure prediction

Zhao, R., Yin, S., Zhang, Q. C., & Sun, L. (2025) · Science China Life Sciences · doi:10.1007/s11427-025-3079-3

RNA Structure and Its Function

Sumi, S., Ito, F., Xu, R., Saito, H., & Kawaguchi, R. K. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5284-8_11

‘Artificial intelligence and machine learning in RNA biology’

Zarnack, K., & Eyras, E. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad415 · Cited by 14 · see citing works

Integrated experimental and AI innovations for RNA structure determination

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