Discovery of RNA-Targeting Small Molecules: Challenges and Future Directions

Abstract

RNA-targeting small molecules represent a transformative frontier in drug discovery, offering novel therapeutic avenues for diseases traditionally deemed undruggable. This review explores the latest advancements in the development of RNA-binding small molecules, focusing on the current obstacles and promising avenues for future research. We highlight innovations in RNA structure determination, including X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, which provide the foundation for rational drug design. The role of computational approaches, such as deep learning and molecular docking, is emphasized for enhancing RNA structure prediction and ligand screening efficiency. Additionally, we discuss the utility of focused libraries, DNA-encoded libraries, and small-molecule microarrays in identifying bioactive ligands, alongside the potential of fragment-based drug discovery for exploring chemical space. Emerging strategies, such as RNA degraders and modulators of RNA–protein interactions, are reviewed for their therapeutic promise. Specifically, we underscore the pivotal role of artificial intelligence and machine learning in accelerating discovery and optimizing RNA-targeted therapeutics. By synthesizing these advancements, this review aims to inspire further research and collaboration, unlocking the full potential of RNA-targeting small molecules to revolutionize treatment paradigms for a wide range of diseases.

Keywords

bioactive small molecules / computer-aided design / machine learning / RNA:protein interactions / RNA-degrader / RNA-targeting

References

[1] I. Dunham, A. Kundaje, S. F. Aldred, et al., “An Integrated Encyclopedia of DNA Elements in the human Genome,” Nature 489, no. 7414 (2012): 57-74.

[2] J. P. Overington, B. Al-Lazikani, A. L. Hopkins, “How Many Drug Targets Are There?,” Nature Reviews Drug Discovery 5, no. 12 (2006): 993-996.

[3] S. Surade, L. Blundell Tom, “Structural Biology and Drug Discovery of Difficult Targets: The Limits of Ligandability,” Chemistry & Biology 19, no. 1 (2012): 42-50.

[4] M. Schlander, K. Hernandez-Villafuerte, C.-Y. Cheng, J. Mestre-Ferrandiz, M. Baumann, “How Much Does It Cost to Research and Develop a New Drug? A Systematic Review and Assessment,” Pharmacoeconomics 39, no. 11 (2021): 1243-1269.

[5] L. S. McCoy, Y. Xie, Y. Tor, “Antibiotics That Target Protein Synthesis,” WIREs RNA 2, no. 2 (2011): 209-232.

[6] A. Serganov, E. Nudler, “A Decade of Riboswitches,” Cell 152, no. 1 (2013): 17-24.

[7] S. Kovachka, M. Panosetti, B. Grimaldi, S. Azoulay, A. Di Giorgio, M. Duca, “Small Molecule Approaches to Targeting RNA,” Nature Reviews Chemistry 8, no. 2 (2024): 120-135.

[8] J. R. Thomas, P. J. Hergenrother, “Targeting RNA With Small Molecules,” Chemical Reviews 108, no. 4 (2008): 1171-1224.

[9] L. Fang, W. A. Velema, Y. Lee, et al., “Pervasive Transcriptome Interactions of Protein-targeted Drugs,” Nature Chemistry 15, no. 10 (2023): 1374-1383.

[10] Q. Chen, Y. Li, C. Lin, et al., “Expanding the DNA-encoded Library Toolbox: Identifying Small Molecules Targeting RNA,” Nucleic Acids Research 50, no. 12 (2022): e67-e67.

[11] K. P. Lundquist, V. Panchal, C. H. Gotfredsen, R. Brenk, M. H. Clausen, “Fragment-Based Drug Discovery for RNA Targets,” Chemmedchem 16, no. 17 (2021): 2588-2603.

[12] G. Bagnolini, T. B. Luu, A. E. Hargrove, “Recognizing the Power of Machine Learning and Other Computational Methods to Accelerate Progress in Small Molecule Targeting of RNA,” Rna 29, no. 4 (2023): 473-488.

[13] J. Manigrasso, M. Marcia, M. De Vivo, “Computer-aided Design of RNA-targeted Small Molecules: A Growing Need in Drug Discovery,” Chemistry 7, no. 11 (2021): 2965-2988.

[14] C. R. Neil, C. Schaening-Burgos, M. S. Alexis, et al., “Poison Exons: Tuning RNA Splicing for Targeted Gene Regulation,” Trends in Pharmacological Sciences 46, no. 3 (2025): 264-278.

[15] L. Bouton, A. Ecoutin, F. Malard, S. Campagne, “Small Molecules Modulating RNA Splicing: A Review of Targets and Future Perspectives,” RSC Medicinal Chemistry 15, no. 4 (2024): 1109-1126.

[16] S. Dhillon, “Risdiplam: First Approval,” Drugs 80, no. 17 (2020): 1853-1858.

[17] Y. Tong, Y. Lee, X. Liu, et al., “Programming Inactive RNA-binding Small Molecules Into Bioactive Degraders,” Nature 618, no. 7963 (2023): 169-179.

[18] C. Rhodes, S. Balaratnam, K. Yazdani, S. Seshadri, J. S. Schneekloth, “Targeting RNA-protein Interactions With Small Molecules: Promise and Therapeutic Potential,” Medicinal Chemistry Research 33, no. 11 (2024): 2050-2065.

[19] J. L. Childs-Disney, X. Yang, Q. M. R. Gibaut, Y. Tong, R. T. Batey, M. D. Disney, “Targeting RNA Structures With Small Molecules,” Nature Reviews Drug Discovery 21, no. 10 (2022): 736-762.

[20] K. D. Warner, C. E. Hajdin, K. M. Weeks, “Principles for Targeting RNA With Drug-Like Small Molecules,” Nature Reviews Drug Discovery 17, no. 8 (2018): 547-558.

[21] J. P. Falese, A. Donlic, A. E. Hargrove, “Targeting RNA With Small Molecules: From Fundamental Principles towards the Clinic,” Chemical Society Reviews 50, no. 4 (2021): 2224-2243.

[22] A.-M. Yu, Y. H. Choi, M.-J. Tu, “RNA Drugs and RNA Targets for Small Molecules: Principles, Progress, and Challenges,” Pharmacological Reviews 72, no. 4 (2020): 862-898.

[23] X. Hong, J. Zheng, J. Xie, et al., “RR3DD: An RNA Global Structure-based RNA Three-dimensional Structural Classification Database,” RNA Biology 18, no. sup2 (2021): 738-746.

[24] A. Ponce-Salvatierra, Astha, K. Merdas, et al., “Computational Modeling of RNA 3D Structure Based on Experimental Data,” Bioscience Reports 39, no. 2 (2019): BSR20180430.

[25] I. Tinoco, C. Bustamante, “How RNA Folds,” Journal of Molecular Biology 293, no. 2 (1999): 271-281.

[26] D. W. Celander, T. R. Cech, “Visualizing the Higher Order Folding of a Catalytic RNA Molecule,” Science 251, no. 4992 (1991): 401-407.

[27] P. P. Zarrinkar, J. R. Williamson, “Kinetic Intermediates in RNA Folding,” Science 265, no. 5174 (1994): 918-924.

[28] Q. Zhao, Z. Zhao, X. Fan, Z. Yuan, Q. Mao, Y. Yao, “Review of Machine Learning Methods for RNA Secondary Structure Prediction,” Plos Computational Biology 17, no. 8 (2021): e1009291.

[29] T. Xia, J. SantaLucia, M. E. Burkard, et al., “Thermodynamic Parameters for an Expanded Nearest-Neighbor Model for Formation of RNA Duplexes With Watson−Crick Base Pairs,” Biochemistry 37, no. 42 (1998): 14719-14735.

[30] D. H. Turner, D. H. Mathews, “NNDB: The Nearest Neighbor Parameter Database for Predicting Stability of Nucleic Acid Secondary Structure,” Nucleic Acids Research 38, no. suppl_1 (2010): D280-D282.

[31] M. Rehmsmeier, P. Steffen, M. Hochsmann, R. Giegerich, “Fast and Effective Prediction of microRNA/Target Duplexes,” Rna 10, no. 10 (2004): 1507-17.

[32] X. Tang, S. Thomas, L. Tapia, D. P. Giedroc, N. M. Amato, “Simulating RNA Folding Kinetics on Approximated Energy Landscapes,” Journal of Molecular Biology 381, no. 4 (2008): 1055-67.

[33] M. Andronescu, A. Condon, H. H. Hoos, D. H. Mathews, K. P. Murphy, “Computational Approaches for RNA Energy Parameter Estimation,” Rna 16, no. 12 (2010): 2304-18.

[34] M. Andronescu, A. Condon, H. H. Hoos, D. H. Mathews, K. P. Murphy, “Efficient Parameter Estimation for RNA Secondary Structure Prediction,” Bioinformatics 23, no. 13 (2007): i19-i28.

[35] J. S. Reuter, D. H. Mathews, “RNAstructure: Software for RNA Secondary Structure Prediction and Analysis,” BMC Bioinformatics [Electronic Resource] 11, no. 1 (2010): 129.

[36] M. Zuker, P. Stiegler, “Optimal Computer Folding of Large RNA Sequences Using Thermodynamics and Auxiliary Information,” Nucleic Acids Research 9, no. 1 (1981): 133-148.

[37] R. Lorenz, S. H. Bernhart, C. Höner Zu Siederdissen, et al., “ViennaRNA Package 2.0,” Algorithms for Molecular Biology 6, no. 1 (2011): 26.

[38] L. Huang, H. Zhang, D. Deng, et al., “LinearFold: Linear-time Approximate RNA Folding by 5'-to-3' dynamic Programming and Beam Search,” Bioinformatics 35, no. 14 (2019): i295-i304.

[39] R. D. Dowell, S. R. Eddy, “Evaluation of Several Lightweight Stochastic Context-free Grammars for RNA Secondary Structure Prediction,” BMC Bioinformatics [Electronic Resource] 5 (2004): 71.

[40] B. Knudsen, J. Hein, “Pfold: RNA Secondary Structure Prediction Using Stochastic Context-free Grammars,” Nucleic Acids Research 31, no. 13 (2003): 3423-8.

[41] C. B. Do, D. A. Woods, S. Batzoglou, “CONTRAfold: RNA Secondary Structure Prediction Without Physics-based Models,” Bioinformatics 22, no. 14 (2006): e90-8.

[42] J. Singh, J. Hanson, K. Paliwal, Y. Zhou, “RNA Secondary Structure Prediction Using an Ensemble of Two-dimensional Deep Neural Networks and Transfer Learning,” Nature Communications 10, no. 1 (2019): 5407.

[43] J. Singh, K. Paliwal, T. Zhang, J. Singh, T. Litfin, Y. Zhou, “Improved RNA Secondary Structure and Tertiary Base-pairing Prediction Using Evolutionary Profile, Mutational Coupling and Two-dimensional Transfer Learning,” Bioinformatics 37, no. 17 (2021): 2589-2600.

[44] L. Fu, Y. Cao, J. Wu, Q. Peng, Q. Nie, X. Xie, “UFold: Fast and Accurate RNA Secondary Structure Prediction With Deep Learning,” Nucleic Acids Research 50, no. 3 (2022): e14.

[45] K. Mao, J. Wang, Y. Xiao, “Length-Dependent Deep Learning Model for RNA Secondary Structure Prediction,” Molecules (Basel, Switzerland) 27, no. 3 (2022): 1030.

[46] E. Yang, H. Zhang, Z. Zang, et al., “GCNfold: A Novel Lightweight Model With Valid Extractors for RNA Secondary Structure Prediction,” Computers in Biology and Medicine 164 (2023): 107246.

[47] Z. Wang, Y. Feng, Q. Tian, Z. Liu, P. Yan, X. Li, “RNADiffFold: Generative RNA Secondary Structure Prediction Using Discrete Diffusion Models,” Brief Bioinform 26, no. 1 (2025): bbae618.

[48] N. A. Siegfried, S. Busan, G. M. Rice, J. A. E. Nelson, K. M. Weeks, “RNA Motif Discovery by SHAPE and Mutational Profiling (SHAPE-MaP),” Nature Methods 11, no. 9 (2014): 959-965.

[49] M. Zubradt, P. Gupta, S. Persad, A. M. Lambowitz, J. S. Weissman, S. Rouskin, “DMS-MaPseq for Genome-wide or Targeted RNA Structure Probing in Vivo,” Nature Methods 14, no. 1 (2017): 75-82.

[50] S. Busan, C. A. Weidmann, A. Sengupta, K. M. Weeks, “Guidelines for SHAPE Reagent Choice and Detection Strategy for RNA Structure Probing Studies,” Biochemistry 58, no. 23 (2019): 2655-2664.

[51] R. C. Spitale, P. Crisalli, R. A. Flynn, E. A. Torre, E. T. Kool, H. Y. Chang, “RNA SHAPE Analysis in Living Cells,” Nature Chemical Biology 9, no. 1 (2013): 18-20.

[52] P. J. Tomezsko, V. D. A. Corbin, P. Gupta, et al., “Determination of RNA Structural Diversity and Its Role in HIV-1 RNA Splicing,” Nature 582, no. 7812 (2020): 438-442.

[53] S. H. Kim, G. J. Quigley, F. L. Suddath, et al., “Three-Dimensional Structure of Yeast Phenylalanine Transfer RNA: Folding of the Polynucleotide Chain,” Science 179, no. 4070 (1973): 285-288.

[54] F. P. Panei, R. Torchet, H. Ménager, P. Gkeka, M. Bonomi, “HARIBOSS: A Curated Database of RNA-small Molecules Structures to Aid Rational Drug Design,” Bioinformatics 38, no. 17 (2022): 4185-4193.

[55] N. Pujari, S. L. Saundh, F. A. Acquah, B. H. M. Mooers, A. R. Ferré-D'Amaré, A. K. Leung, “Engineering Crystal Packing in RNA Structures I: Past and Future Strategies for Engineering RNA Packing in Crystals,” Crystals 11, no. 8 (2021): 952.

[56] A. R. Ferré-D'Amaré, J. A. Doudna, “Methods to Crystallize RNA,” Current Protocols in Nucleic Acid Chemistry 1, no. 1 (2000): 7.6.1-7.6.13.

[57] J.-I. Tomizawa, “Control of Cole 1 Plasmid Replication: The Process of Binding of RNA I to the Primer Transcript,” Cell 38, no. 3 (1984): 861-870.

[58] J. C. Paillart, E. Skripkin, B. Ehresmann, C. Ehresmann, R. Marquet, “A Loop-loop “Kissing” Complex Is the Essential Part of the Dimer Linkage of Genomic HIV-1 RNA,” Proceedings of the National Academy of Sciences of the United States of America 93, no. 11 (1996): 5572-5577.

[59] S. Shetty, S. Kim, T. Shimakami, S. M. Lemon, M.-R. Mihailescu, “Hepatitis C Virus Genomic RNA Dimerization Is Mediated via a Kissing Complex Intermediate,” Rna 16, no. 5 (2010): 913-925.

[60] J. A. Howe, H. Wang, T. O. Fischmann, et al., “Selective Small-molecule Inhibition of an RNA Structural Element,” Nature 526, no. 7575 (2015): 672-677.

[61] J. A. Howe, L. Xiao, T. O. Fischmann, et al., “Atomic Resolution Mechanistic Studies of Ribocil: A Highly Selective Unnatural Ligand Mimic of the E. coli FMN Riboswitch,” RNA Biology 13, no. 10 (2016): 946-954.

[62] N. K. Conrad, “The Emerging Role of Triple Helices in RNA Biology,” WIREs RNA 5, no. 1 (2014): 15-29.

[63] F. A. Abulwerdi, W. Xu, A. A. Ageeli, et al., “Selective Small-Molecule Targeting of a Triple Helix Encoded by the Long Noncoding RNA, MALAT1,” ACS Chemical Biology 14, no. 2 (2019): 223-235.

[64] P. J. Lukavsky, J. D. Puglisi, “Structure Determination of Large Biological RNAs,” Methods in Enzymology 394 (2005): 399-416.

[65] L. Braun, Z. Alirezaeizanjani, R. Tesch, H. Kooshapur, “High-Resolution Structures of RNA. RNA as a Drug Target,” Methods and Principles in Medicinal Chemistry (2024): 29-48.

[66] R. P. Barnwal, F. Yang, G. Varani, “Applications of NMR to Structure Determination of RNAs Large and Small,” Archives of Biochemistry and Biophysics 628 (2017): 42-56.

[67] M. P. Latham, D. J. Brown, S. A. McCallum, A. Pardi, “NMR Methods for Studying the Structure and Dynamics of RNA,” Chembiochem 6, no. 9 (2005): 1492-1505.

[68] R. P. Barnwal, E. Loh, K. S. Godin, et al., “Structure and Mechanism of a Molecular Rheostat, an RNA Thermometer That Modulates Immune Evasion by Neisseria meningitidis,” Nucleic Acids Research 44, no. 19 (2016): 9426-9437.

[69] R. Roy, A. Geng, S. Pratihar, H. Shi, Al-Hashimi HM. RNA Conformational Ensembles From NMR Residual Dipolar Couplings. In: Yao L, Vogeli B, eds. “Residual Dipolar Couplings: Principles and Applications” (Royal Society of Chemistry, 2024): 0.

[70] L. R. Ganser, J. Lee, A. Rangadurai, et al., “High-performance Virtual Screening by Targeting a High-resolution RNA Dynamic Ensemble,” Nature Structural & Molecular Biology 25, no. 5 (2018): 425-434.

[71] Y. Cheng, N. Grigorieff, A. Penczek Pawel, T. Walz, “A Primer to Single-Particle Cryo-Electron Microscopy,” Cell 161, no. 3 (2015): 438-449.

[72] H. Ma, X. Jia, K. Zhang, Z. Su, “Cryo-EM Advances in RNA Structure Determination,” Signal Transduction and Targeted Therapy 7, no. 1 (2022): 58.

[73] G. Qu, P. S. Kaushal, J. Wang, et al., “Structure of a Group II Intron in Complex With Its Reverse Transcriptase,” Nature Structural & Molecular Biology 23, no. 6 (2016): 549-57.

[74] D. Liu, F. A. Thélot, J. A. Piccirilli, M. Liao, P. Yin, “Sub-3-Å Cryo-EM Structure of RNA Enabled by Engineered Homomeric Self-assembly,” Nature Methods 19, no. 5 (2022): 576-585.

[75] K. Zhang, S. C. Keane, Z. Su, et al., “Structure of the 30 kDa HIV-1 RNA Dimerization Signal by a Hybrid Cryo-EM, NMR, and Molecular Dynamics Approach,” Structure (London, England) 26, no. 3 (2018): 490-498. e3.

[76] S. Li, Z. Su, J. Lehmann, et al., “Structural Basis of Amino Acid Surveillance by Higher-order tRNA-mRNA Interactions,” Nature Structural & Molecular Biology 26, no. 12 (2019): 1094-1105.

[77] X. Ou, Y. Zhang, Y. Xiong, Y. Xiao, “Advances in RNA 3D Structure Prediction,” Journal of Chemical Information and Modeling 62, no. 23 (2022): 5862-5874.

[78] M. J. Boniecki, G. Lach, W. K. Dawson, et al., “SimRNA: A Coarse-grained Method for RNA Folding Simulations and 3D Structure Prediction,” Nucleic Acids Research 44, no. 7 (2016): e63-e63.

[79] S. Pasquali, P. Derreumaux, “HiRE-RNA: A High Resolution Coarse-Grained Energy Model for RNA,” The Journal of Physical Chemistry B 114, no. 37 (2010): 11957-11966.

[80] P. Xiong, R. Wu, J. Zhan, Y. Zhou, “Pairing a High-resolution Statistical Potential With a Nucleobase-centric Sampling Algorithm for Improving RNA Model Refinement,” Nature Communications 12, no. 1 (2021): 2777.

[81] R. Das, D. Baker, “Automated De Novoprediction of Native-Like RNA Tertiary Structures,” Proceedings of the National Academy of Sciences of the United States of America 104, no. 37 (2007): 14664-14669.

[82] R. Das, J. Karanicolas, D. Baker, “Atomic Accuracy in Predicting and Designing Noncanonical RNA Structure,” Nature Methods 7, no. 4 (2010): 291-294.

[83] C. Y. Cheng, F. C. Chou, R. Das, “Modeling Complex RNA Tertiary Folds With Rosetta,” Methods in Enzymology 553 (2015): 35-64.

[84] A. M. Watkins, R. Rangan, R. Das, “FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds,” Structure (London, England) 28, no. 8 (2020): 963-976. e6.

[85] M. Popenda, M. Szachniuk, M. Antczak, et al., “Automated 3D Structure Composition for Large RNAs,” Nucleic Acids Research 40, no. 14 (2012): e112.

[86] M. Biesiada, K. J. Purzycka, M. Szachniuk, J. Blazewicz, R. W. Adamiak, “Automated RNA 3D Structure Prediction With RNAComposer,” Methods in Molecular Biology 1490 (2016): 199-215.

[87] T. Shen, Z. Hu, Z. Peng, et al. E2Efold-3D: End-to-end Deep Learning Method for Accurate De Novo RNA 3D Structure Prediction. arXiv preprint arXiv:220701586. 2022;

[88] R. J. L. Townshend, S. Eismann, A. M. Watkins, et al., “Geometric Deep Learning of RNA Structure,” Science 373, no. 6558 (2021): 1047-1051.

[89] W. Wang, C. Feng, R. Han, et al., “trRosettaRNA: Automated Prediction of RNA 3D Structure With Transformer Network,” Nature Communications 14, no. 1 (2023): 7266.

[90] J. Jumper, R. Evans, A. Pritzel, et al., “Highly Accurate Protein Structure Prediction With AlphaFold,” Nature 596, no. 7873 (2021): 583-589.

[91] J. Abramson, J. Adler, J. Dunger, et al., “Accurate Structure Prediction of Biomolecular Interactions With AlphaFold 3,” Nature 630, no. 8016 (2024): 493-500.

[92] C. Nithin, S. Kmiecik, R. Błaszczyk, J. Nowicka, I. Tuszyńska, “Comparative Analysis of RNA 3D Structure Prediction Methods: Towards Enhanced Modeling of RNA-ligand Interactions,” Nucleic Acids Research 52, no. 13 (2024): 7465-7486.

[93] R. T. McDonnell, A. N. Henderson, A. H. Elcock, “Structure Prediction of Large RNAs With AlphaFold3 Highlights Its Capabilities and Limitations,” Journal of Molecular Biology 436, no. 22 (2024): 168816.

[94] J. Wohlwend, G. Corso, S. Passaro, et al., “Boltz-1 Democratizing Biomolecular Interaction Modeling,” BioRxiv (2025). 2024.11.19.624167.

[95] ByteDance AMLAIST, X. Chen, Y. Zhang, et al., ByteDance AMLAIST, “Protenix—Advancing Structure Prediction through a Comprehensive AlphaFold3 Reproduction,” BioRxiv (2025). 2025.01.08.631967.

[96] S. Passaro, G. Corso, J. Wohlwend, et al., “Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction,” BioRxiv (2025). 2025.06.14.659707.

[97] J. Kondo, “A Structural Basis for the Antibiotic Resistance Conferred by an A1408G Mutation in 16S rRNA and for the Antiprotozoal Activity of Aminoglycosides,” Angewandte Chemie International Edition 51, no. 2 (2012): 465-468.

[98] U. Chukwudi Chinwe, “rRNA Binding Sites and the Molecular Mechanism of Action of the Tetracyclines,” Antimicrobial Agents and Chemotherapy 60, no. 8 (2016): 4433-4441.

[99] P. Vannuffel, C. Cocito, “Mechanism of Action of Streptogramins and Macrolides,” Drugs 51, no. 1 (1996): 20-30.

[100] M. R. Barbachyn. Oxazolidinone Antibacterial Agents. In: Dougherty TJ, Pucci MJ, eds. “Antibiotic Discovery and Development” (Springer US, 2012): 271-299.

[101] A. G. Myasnikov, S. Kundhavai Natchiar, M. Nebout, et al., “Structure-function Insights Reveal the human Ribosome as a Cancer Target for Antibiotics,” Nature Communications 7, no. 1 (2016): 12856.

[102] E. M. Welch, E. R. Barton, J. Zhuo, et al., “PTC124 targets Genetic Disorders Caused by Nonsense Mutations,” Nature 447, no. 7140 (2007): 87-91.

[103] E. M. Agency. Translarna: EMA re-confirms non-renewal of authorisation of Duchenne muscular dystrophy medicine.

[104] SOURCE PTC Therapeutics I. PTC Therapeutics Announces FDA Acceptance of Translarna™ NDA Resubmission.

[105] M. Chen, J. L. Manley, “Mechanisms of Alternative Splicing Regulation: Insights From Molecular and Genomics Approaches,” Nature Reviews Molecular Cell Biology 10, no. 11 (2009): 741-754.

[106] M. Salton, T. Misteli, “Small Molecule Modulators of Pre-mRNA Splicing in Cancer Therapy,” Trends in Molecular Medicine 22, no. 1 (2016): 28-37.

[107] M. Taniguchi-Ikeda, K. Kobayashi, M. Kanagawa, et al., “Pathogenic Exon-trapping by SVA Retrotransposon and Rescue in Fukuyama Muscular Dystrophy,” Nature 478, no. 7367 (2011): 127-131.

[108] B. Perrone, V. La Cognata, T. Sprovieri, et al., “Alternative Splicing of ALS Genes: Misregulation and Potential Therapies,” Cellular and Molecular Neurobiology 40, no. 1 (2020): 1-14.

[109] A. López-Martínez, P. Soblechero-Martín, L. de-la-Puente-Ovejero, G. Nogales-Gadea, V. Arechavala-Gomeza, “An Overview of Alternative Splicing Defects Implicated in Myotonic Dystrophy Type I,” Genes 11, no. 9 (2020): 1109.

[110] R. K. Singh, T. A. Cooper, “Pre-mRNA Splicing in Disease and Therapeutics,” Trends in Molecular Medicine 18, no. 8 (2012): 472-482.

[111] S. K. Custer, T. D. Gilson, H. Li, et al., “Altered mRNA Splicing in SMN-Depleted Motor Neuron-Like Cells,” PLoS ONE 11, no. 10 (2016): e0163954.

[112] S. Lefebvre, P. Burlet, Q. Liu, et al., “Correlation Between Severity and SMN Protein Level in Spinal Muscular Atrophy,” Nature Genetics 16, no. 3 (1997): 265-269.

[113] N. A. Naryshkin, M. Weetall, A. Dakka, et al., “SMN2 splicing Modifiers Improve Motor Function and Longevity in Mice With Spinal Muscular Atrophy,” Science 345, no. 6197 (2014): 688-693.

[114] H. Ratni, G. M. Karp, M. Weetall, et al., “Specific Correction of Alternative Survival Motor Neuron 2 Splicing by Small Molecules: Discovery of a Potential Novel Medicine To Treat Spinal Muscular Atrophy,” Journal of Medicinal Chemistry 59, no. 13 (2016): 6086-6100.

[115] A. Poirier, M. Weetall, K. Heinig, et al., “Risdiplam Distributes and Increases SMN Protein in both the central Nervous System and Peripheral Organs,” Pharmacology Research & Perspectives 6, no. 6 (2018): e00447.

[116] H. Ratni, M. Ebeling, J. Baird, et al., “Discovery of Risdiplam, a Selective Survival of Motor Neuron-2 (SMN2) Gene Splicing Modifier for the Treatment of Spinal Muscular Atrophy (SMA),” Journal of Medicinal Chemistry 61, no. 15 (2018): 6501-6517.

[117] B. Torroba, M. Natsuko, M. HE, et al., “RNA-based Drug Discovery for Spinal Muscular Atrophy: A Story of Small Molecules and Antisense Oligonucleotides,” Expert Opinion on Drug Discovery 18, no. 2 (2023): 181-192.

[118] Y. Ishigami, M. S. Wong, C. Martí-Gómez, et al., “Specificity, Synergy, and Mechanisms of Splice-modifying Drugs,” Nature Communications 15, no. 1 (2024): 1880.

[119] U. S. Haque, T. Yokota, “Recent Progress in Gene-Targeting Therapies for Spinal Muscular Atrophy: Promises and Challenges,” Genes 15, no. 8 (2024): 999.

[120] C. Sampaio, “Huntington Disease - Update on Ongoing Therapeutic Developments and a Look Toward the Future,” Parkinsonism & Related Disorders 122 (2024): 106049.

[121] J. R. Lytle, T. A. Yario, J. A. Steitz, “Target mRNAs Are Repressed as Efficiently by microRNA-binding Sites in the 5′ UTR as in the 3′ UTR,” Proceedings of the National Academy of Sciences of the United States of America 104, no. 23 (2007): 9667-9672.

[122] A. Brümmer, J. Hausser, “MicroRNA Binding Sites in the Coding Region of mRNAs: Extending the Repertoire of Post-transcriptional Gene Regulation,” BioEssays 36, no. 6 (2014): 617-626.

[123] R. F. Place, L.-C. Li, D. Pookot, E. J. Noonan, R. Dahiya, “MicroRNA-373 Induces Expression of Genes With Complementary Promoter Sequences,” Proceedings of the National Academy of Sciences of the United States of America 105, no. 5 (2008): 1608-1613.

[124] N. T. Schirle, J. Sheu-Gruttadauria, I. J. MacRae, “Structural Basis for microRNA Targeting,” Science 346, no. 6209 (2014): 608-613.

[125] R. C. Friedman, K. K.-H. Farh, C. B. Burge, D. P. Bartel, “Most Mammalian mRNAs Are Conserved Targets of microRNAs,” Genome Research 19, no. 1 (2009): 92-105.

[126] K. U. Tüfekci, M. G. Öner, R. L. J. Meuwissen, Ş. Genç The Role of MicroRNAs in Human Diseases. In: Yousef M, Allmer J, eds. “miRNomics: MicroRNA Biology and Computational Analysis” (Humana Press, 2014): 33-50.

[127] M. Ha, V. N. Kim, “Regulation of microRNA Biogenesis,” Nature Reviews Molecular Cell Biology 15, no. 8 (2014): 509-524.

[128] J. O'Brien, H. Hayder, Y. Zayed, C. Peng, “Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation,” Front Endocrinol (Lausanne) 9 (2018): 2018.

[129] J. C. Medley, G. Panzade, A. Y. Zinovyeva, “microRNA Strand Selection: Unwinding the Rules,” WIREs RNA 12, no. 3 (2021): e1627.

[130] P. C. Monroig, L. Chen, S. Zhang, G. A. Calin, “Small Molecule Compounds Targeting miRNAs for Cancer Therapy,” Advanced Drug Delivery Reviews 81 (2015): 104-116.

[131] S. P. Velagapudi, S. M. Gallo, M. D. Disney, “Sequence-based Design of Bioactive Small Molecules That Target Precursor microRNAs,” Nature Chemical Biology 10, no. 4 (2014): 291-297.

[132] S. P. Velagapudi, M. D. Cameron, C. L. Haga, et al., “Design of a Small Molecule Against an Oncogenic Noncoding RNA,” Proceedings of the National Academy of Sciences of the United States of America 113, no. 21 (2016): 5898-5903.

[133] S. Nahar, N. Ranjan, A. Ray, D. P. Arya, S. Maiti, “Potent Inhibition of miR-27a by Neomycin-bisbenzimidazole Conjugates,” Chemical Science 6, no. 10 (2015): 5837-5846.

[134] H. Yan, U. Bhattarai, Z.-F. Guo, F.-S. Liang, “Regulating miRNA-21 Biogenesis by Bifunctional Small Molecules,” Journal of the American Chemical Society 139, no. 14 (2017): 4987-4990.

[135] D. D. Vo, T. P. A. Tran, C. Staedel, et al., “Oncogenic MicroRNAs Biogenesis as a Drug Target: Structure-Activity Relationship Studies on New Aminoglycoside Conjugates,” Chemistry - A European Journal 22, no. 15 (2016): 5350-5362.

[136] K. Azzaoui, M. Blommers, M. Götte, K. Zimmermann, H. Liu, H. Fretz, “Discovery of Small Molecule Drugs Targeting the Biogenesis of microRNA-155 for the Treatment of Systemic Lupus Erythematosus,” Chimia 74, no. 10 (2020): 798.

[137] A. Donlic, E. G. Swanson, L. Y. Chiu, et al., “R-BIND 2.0: An Updated Database of Bioactive RNA-Targeting Small Molecules and Associated RNA Secondary Structures,” Acs Chemical Biology 17, no. 6 (2022): 1556-1566.

[138] G. Pesole, F. Mignone, C. Gissi, G. Grillo, F. Licciulli, S. Liuni, “Structural and Functional Features of Eukaryotic mRNA Untranslated Regions,” Gene 276, no. 1 (2001): 73-81.

[139] D. G. Macejak, P. Sarnow, “Internal Initiation of Translation Mediated by the 5′ Leader of a Cellular mRNA,” Nature 353, no. 6339 (1991): 90-94.

[140] S. R. Thompson, P. Sarnow, “Enterovirus 71 Contains a Type I IRES Element That Functions When Eukaryotic Initiation Factor eIF4G Is Cleaved,” Virology 315, no. 1 (2003): 259-266.

[141] M.-T. Yeh, S.-W. Wang, C.-K. Yu, et al., “A Single Nucleotide in Stem Loop II of 5′-Untranslated Region Contributes to Virulence of Enterovirus 71 in Mice,” PLoS ONE 6, no. 11 (2011): e27082.

[142] J. Davila-Calderon, N. N. Patwardhan, L.-Y. Chiu, et al., “IRES-targeting Small Molecule Inhibits Enterovirus 71 Replication via Allosteric Stabilization of a Ternary Complex,” Nature Communications 11, no. 1 (2020): 4775.

[143] M. W. Hentze, S. W. Caughman, T. A. Rouault, et al., “Identification of the Iron-Responsive Element for the Translational Regulation of Human Ferritin mRNA,” Science 238, no. 4833 (1987): 1570-1573.

[144] J. T. Rogers, C. M. Cahill, “Iron-responsive-Like Elements and Neurodegenerative Ferroptosis,” Learning & Memory (Cold Spring Harbor, N.Y.) 27, no. 9 (2020): 395-413.

[145] J. T. Rogers, S. Mikkilineni, I. Cantuti-Castelvetri, et al., “The Alpha-synuclein 5′Untranslated Region Targeted Translation Blockers: Anti-alpha Synuclein Efficacy of Cardiac Glycosides and Posiphen,” Journal of Neural Transmission 118, no. 3 (2011): 493-507.

[146] P. Zhang, H.-J. Park, J. Zhang, et al., “Translation of the Intrinsically Disordered Protein α-synuclein Is Inhibited by a Small Molecule Targeting Its Structured mRNA,” Proceedings of the National Academy of Sciences of the United States of America 117, no. 3 (2020): 1457-1467.

[147] T. T. Wager, X. Hou, P. R. Verhoest, A. Villalobos, “Moving Beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach To Enable Alignment of Druglike Properties,” Acs Chemical Neuroscience 1, no. 6 (2010): 435-449.

[148] Y. Tong, P. Zhang, X. Yang, et al., “Decreasing the Intrinsically Disordered Protein α-synuclein Levels by Targeting Its Structured mRNA With a Ribonuclease-targeting Chimera,” Proceedings of the National Academy of Sciences 121, no. 2 (2024): e2306682120.

[149] S. Chatterjee, J. K. Pal, “Role of 5′- and 3′-untranslated Regions of mRNAs in human Diseases,” Biology of the Cell 101, no. 5 (2009): 251-262.

[150] M. Fardaei, K. Larkin, J. D. Brook, M. G. Hamshere, “In Vivo co-localisation of MBNL Protein With DMPK Expanded-repeat Transcripts,” Nucleic Acids Research 29, no. 13 (2001): 2766-2771.

[151] A. Chau, A. Kalsotra, “Developmental Insights Into the Pathology of and Therapeutic Strategies for DM1: Back to the Basics,” Developmental Dynamics 244, no. 3 (2015): 377-390.

[152] S. G. Rzuczek, M. R. Southern, M. D. Disney, “Studying a Drug-Like, RNA-Focused Small Molecule Library Identifies Compounds That Inhibit RNA Toxicity in Myotonic Dystrophy,” ACS Chemical Biology 10, no. 12 (2015): 2706-2715.

[153] L. Nguyen, L. M. Luu, S. Peng, J. F. Serrano, H. Y. E. Chan, S. C. Zimmerman, “Rationally Designed Small Molecules That Target both the DNA and RNA Causing Myotonic Dystrophy Type 1,” Journal of the American Chemical Society 137, no. 44 (2015): 14180-14189.

[154] S. M. Hoy, “Nusinersen: First Global Approval,” Drugs 77, no. 4 (2017): 473-479.

[155] C. A. Ross, S. J. Tabrizi, “Huntington's Disease: From Molecular Pathogenesis to Clinical Treatment,” The Lancet Neurology 10, no. 1 (2011): 83-98.

[156] Z. L. McLean, D. Gao, K. Correia, et al., “Splice Modulators Target PMS1 to Reduce Somatic Expansion of the Huntington's Disease-associated CAG Repeat,” Nature Communications 15, no. 1 (2024): 3182.

[157] X. Piao, D. Li, H. Liu, Q. Guo, Y. Yu, “Advances in Gene and Cellular Therapeutic Approaches for Huntington's Disease,” Protein & Cell (2024): pwae042.

[158] D. T. Michaeli, T. Michaeli, S. Albers, T. Boch, J. C. Michaeli, “Special FDA Designations for Drug Development: Orphan, Fast Track, Accelerated Approval, Priority Review, and Breakthrough Therapy,” The European Journal of Health Economics 25, no. 6 (2024): 979-997.

[159] A. Bhattacharyya, C. R. Trotta, J. Narasimhan, et al., “Small Molecule Splicing Modifiers With Systemic HTT-lowering Activity,” Nature Communications 12, no. 1 (2021): 7299.

[160] S. Prajapati, M. Cameron, B. M. Dunyak, et al., “REM-422, a Potent, Selective, Oral Small Molecule mRNA Degrader of the MYB Oncogene, Demonstrates Anti-Tumor Activity in Mouse Xenograft Models of AML,” Blood 142 (2023): 1425.

[161] S. Xi, P. Soulard, K. Li, et al., “Effect of RGT-61159 on Inhibition of Oncogene c-MYB Synthesis and Tumor Growth Inhibition in a Broad Range of ACC PDX Models, at Well Tolerated Doses in Rodents and Non-human Primates,” Journal of Clinical Oncology 42, no. 16_suppl (2024): 6107-6107.

[162] A. Gerson-Gurwitz, N. P. Young, V. K. Goel, et al., “Zotatifin, an eIF4A-Selective Inhibitor, Blocks Tumor Growth in Receptor Tyrosine Kinase Driven Tumors,” Frontiers in Oncology 11 (2021): 2021.

[163] A. Z. Andreou, D. Klostermeier, “eIF4B and eIF4G Jointly Stimulate eIF4A ATPase and Unwinding Activities by Modulation of the eIF4A Conformational Cycle,” Journal of Molecular Biology 426, no. 1 (2014): 51-61.

[164] E. Rosen, M. Sharma, D. Berz, et al., “Phase 1/2 Dose Expansion Study Evaluating First-in-class eIF4A Inhibitor Zotatifin in Patients With ER+ Metastatic Breast Cancer,” Journal of Clinical Oncology 41, no. 16_suppl (2023): 1080-1080.

[165] A. Brown, S. Shao, J. Murray, R. S. Hegde, V. Ramakrishnan, “Structural Basis for Stop Codon Recognition in Eukaryotes,” Nature 524, no. 7566 (2015): 493-496.

[166] J. P. L. Coelho, M. C. J. Yip, K. Oltion, J. Taunton, S. Shao, “The eRF1 Degrader SRI-41315 Acts as a Molecular Glue at the Ribosomal Decoding Center,” Nature Chemical Biology 20, no. 7 (2024): 877-884.

[167] S. Chen, Q. Mao, H. Cheng, W. Tai, “RNA-Binding Small Molecules in Drug Discovery and Delivery: An Overview From Fundamentals,” Journal of Medicinal Chemistry 67, no. 18 (2024): 16002-16017.

[168] S. E. McGeary, K. S. Lin, C. Y. Shi, et al., “The Biochemical Basis of microRNA Targeting Efficacy,” Science 366, no. 6472 (2019): eaav1741.

[169] Y. Chen, X. Wang, “miRDB: An Online Database for Prediction of Functional microRNA Targets,” Nucleic Acids Research 48, no. D1 (2020): D127-D131.

[170] H. Y. Huang, Y. C. Lin, S. Cui, et al., “miRTarBase Update 2022: An Informative Resource for Experimentally Validated miRNA-target Interactions,” Nucleic Acids Research 50, no. D1 (2022): D222-D230.

[171] O. Beylerli, A. Beilerli, T. Ilyasova, A. Shumadalova, H. Shi, A. Sufianov, “CircRNAs in Alzheimer's Disease: What Are the Prospects?,” Noncoding RNA Research 9, no. 1 (2024): 203-210.

[172] J. L. Childs-Disney, X. Y. Yang, Q. M. R. Gibaut, Y. Q. Tong, R. T. Batey, M. D. Disney, “Targeting RNA Structures With Small Molecules,” Nature Reviews Drug Discovery 21, no. 10 (2022): 736-762.

[173] N. F. Rizvi, J. P. Santa Maria,, A. Nahvi, et al., “Targeting RNA With Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space,” SLAS Discovery 25, no. 4 (2020): 384-396.

[174] K. Yazdani, D. Jordan, M. Yang, et al., “Machine Learning Informs RNA-Binding Chemical Space,” Angewandte Chemie (International ed in English) 62, no. 11 (2023): e202211358.

[175] E. P. Nawrocki, D. L. Kolbe, S. R. Eddy, “Infernal 1.0: Inference of RNA Alignments,” Bioinformatics 25, no. 10 (2009): 1335-1337.

[176] S. Sun, J. Yang, Z. Zhang, “RNALigands: A Database and Web Server for RNA-ligand Interactions,” Rna 28, no. 2 (2022): 115-122.

[177] M. D. Disney, A. M. Winkelsas, S. P. Velagapudi, M. Southern, M. Fallahi, J. L. Childs-Disney, “Inforna 2.0: A Platform for the Sequence-Based Design of Small Molecules Targeting Structured RNAs,” ACS Chemical Biology 11, no. 6 (2016): 1720-1728.

[178] T. D. Schneider, G. D. Stormo, L. Gold, A. Ehrenfeucht, “Information Content of Binding Sites on Nucleotide Sequences,” Journal of Molecular Biology 188, no. 3 (1986): 415-31.

[179] J. M. Carothers, S. C. Oestreich, J. H. Davis, J. W. Szostak, “Informational Complexity and Functional Activity of RNA Structures,” Journal of the American Chemical Society 126, no. 16 (2004): 5130-5137.

[180] G. Padroni, N. N. Patwardhan, M. Schapira, A. E. Hargrove, “Systematic Analysis of the Interactions Driving Small Molecule-RNA Recognition,” RSC Medicinal Chemistry 11, no. 7 (2020): 802-813.

[181] N. A. Szulc, Z. Mackiewicz, J. M. Bujnicki, F. Stefaniak, “fingeRNAt—A Novel Tool for High-throughput Analysis of Nucleic Acid-ligand Interactions,” Plos Computational Biology 18, no. 6 (2022): e1009783.

[182] Y. Zhou, Y. Jiang, S. J. Chen, “RNA -ligand Molecular Docking: Advances and Challenges,” WIREs Computational Molecular Science 12, no. 3 (2022): e1571.

[183] P. T. Lang, S. R. Brozell, S. Mukherjee, et al., “DOCK 6: Combining Techniques to Model RNA-small Molecule Complexes,” Rna 15, no. 6 (2009): 1219-1230.

[184] S. Ruiz-Carmona, D. Alvarez-Garcia, N. Foloppe, et al., “rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids,” Plos Computational Biology 10, no. 4 (2014): e1003571.

[185] J. An, M. Totrov, R. Abagyan, “Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes,” Molecular & Cellular Proteomics 4, no. 6 (2005): 752-61.

[186] R. Harris, A. J. Olson, D. S. Goodsell, “Automated Prediction of Ligand-binding Sites in Proteins,” Proteins 70, no. 4 (2008): 1506-17.

[187] P. Zeng, J. Li, W. Ma, Q. Cui, “Rsite: A Computational Method to Identify the Functional Sites of Noncoding RNAs,” Scientific Reports 5, no. 1 (2015): 9179.

[188] P. Zeng, Q. Cui, “Rsite2: An Efficient Computational Method to Predict the Functional Sites of Noncoding RNAs,” Scientific Reports 6, no. 1 (2016): 19016.

[189] K. Wang, Y. Jian, H. Wang, C. Zeng, Y. Zhao, “RBind: Computational Network Method to Predict RNA Binding Sites,” Bioinformatics 34, no. 18 (2018): 3131-3136.

[190] H. Wang, Y. Zhao, “RBinds: A User-friendly Server for RNA Binding Site Prediction,” Computational and Structural Biotechnology Journal 18 (2020): 3762-3765.

[191] H. Su, Z. Peng, J. Yang, “Recognition of Small Molecule-RNA Binding Sites Using RNA Sequence and Structure,” Bioinformatics 37, no. 1 (2021): 36-42.

[192] L.-Z. Sun, Y. Jiang, Y. Zhou, S.-J. Chen, “RLDOCK: A New Method for Predicting RNA-Ligand Interactions,” Journal of Chemical Theory and Computation 16, no. 11 (2020): 7173-7183.

[193] Y. Jiang, S. J. Chen, “RLDOCK Method for Predicting RNA-small Molecule Binding Modes,” Methods (San Diego, Calif.) 197 (2022): 97-105.

[194] G. M. Morris, D. S. Goodsell, R. S. Halliday, et al., “Automated Docking Using a Lamarckian Genetic Algorithm and an Empirical Binding Free Energy Function,” Journal of Computational Chemistry 19, no. 14 (1998): 1639-1662.

[195] C. Guilbert, T. L. James, “Docking to RNA via Root-mean-square-deviation-driven Energy Minimization With Flexible Ligands and Flexible Targets,” Journal of Chemical Information and Modeling 48, no. 6 (2008): 1257-68.

[196] S. D. Morley, M. Afshar, “Validation of an Empirical RNA-ligand Scoring Function for Fast Flexible Docking Using RiboDock®,” Journal of Computer-Aided Molecular Design 18, no. 3 (2004): 189-208.

[197] R. A. Friesner, J. L. Banks, R. B. Murphy, et al., “Glide:  a New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy,” Journal of Medicinal Chemistry 47, no. 7 (2004): 1739-1749.

[198] G. Jones, P. Willett, R. C. Glen, A. R. Leach, R. Taylor, “Development and Validation of a Genetic Algorithm for Flexible Docking,” Journal of Molecular Biology 267, no. 3 (1997): 727-48.

[199] A. C. Stelzer, A. T. Frank, J. D. Kratz, et al., “Discovery of Selective Bioactive Small Molecules by Targeting an RNA Dynamic Ensemble,” Nature Chemical Biology 7, no. 8 (2011): 553-559.

[200] C. Guilbert, T. L. James, “Docking to RNA via Root-Mean-Square-Deviation-Driven Energy Minimization With Flexible Ligands and Flexible Targets,” Journal of Chemical Information and Modeling 48, no. 6 (2008): 1257-1268.

[201] S. Kazemi, D. M. Krüger, F. Sirockin, H. Gohlke, “Elastic Potential Grids: Accurate and Efficient Representation of Intermolecular Interactions for Fully Flexible Docking,” Chemmedchem 4, no. 8 (2009): 1264-8.

[202] D. M. Krüger, J. Bergs, S. Kazemi, H. Gohlke, “Target Flexibility in RNA−Ligand Docking Modeled by Elastic Potential Grids,” Acs Medicinal Chemistry Letters 2, no. 7 (2011): 489-493.

[203] P. Pfeffer, H. Gohlke, “DrugScoreRNA-Knowledge-Based Scoring Function To Predict RNA-Ligand Interactions,” Journal of Chemical Information and Modeling 47, no. 5 (2007): 1868-1876.

[204] W. C. Still, A. Tempczyk, R. C. Hawley, T. Hendrickson, “Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics,” Journal of the American Chemical Society 112, no. 16 (1990): 6127-6129.

[205] F. Fogolari, A. Brigo, H. Molinari, “The Poisson-Boltzmann Equation for Biomolecular Electrostatics: A Tool for Structural Biology,” Journal of Molecular Recognition 15, no. 6 (2002): 377-392.

[206] J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman, C. DA, “Development and Testing of a General Amber Force Field,” Journal of Computational Chemistry 25, no. 9 (2004): 1157-1174.

[207] L. Chen, G. A. Calin, S. Zhang, “Novel Insights of Structure-Based Modeling for RNA-Targeted Drug Discovery,” Journal of Chemical Information and Modeling 52, no. 10 (2012): 2741-2753.

[208] A. Philips, K. Milanowska, G. Lach, J. M. Bujnicki, “LigandRNA: Computational Predictor of RNA-ligand Interactions,” Rna 19, no. 12 (2013): 1605-1616.

[209] C. Oliver, V. Mallet, R. S. Gendron, et al., “Augmented Base Pairing Networks Encode RNA-small Molecule Binding Preferences,” Nucleic Acids Res 48, no. 14 (2020): 7690-7699.

[210] S. Chhabra, J. Xie, A. T. Frank, “RNAPosers: Machine Learning Classifiers for Ribonucleic Acid-Ligand Poses,” The Journal of Physical Chemistry B 124, no. 22 (2020): 4436-4445.

[211] F. Stefaniak, J. M. Bujnicki, “AnnapuRNA: A Scoring Function for Predicting RNA-small Molecule Binding Poses,” PLOS Computational Biology 17, no. 2 (2021): e1008309.

[212] S. R. Krishnan, A. Roy, M. M. Gromiha, “Reliable Method for Predicting the Binding Affinity of RNA-small Molecule Interactions Using Machine Learning,” Brief Bioinform 25, no. 2 (2024): bbae002.

[213] S. Sun, L. Gao, “Contrastive Pre-training and 3D Convolution Neural Network for RNA and Small Molecule Binding Affinity Prediction,” Bioinformatics 40, no. 4 (2024): btae155.

[214] E. Kallert, T. R. Fischer, S. Schneider, M. Grimm, M. Helm, C. Kersten, “Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ ₁ -Riboswitch,” Journal of Chemical Information and Modeling 62, no. 17 (2022): 4134-4148.

[215] D. Jiang, H. Zhao, H. Du, et al., “How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation,” Journal of Chemical Theory and Computation 19, no. 16 (2023): 5633-5647.

[216] H. Zhao, A. Caflisch, “Discovery of ZAP70 Inhibitors by High-throughput Docking Into a Conformation of Its Kinase Domain Generated by Molecular Dynamics,” Bioorganic & Medicinal Chemistry Letters 23, no. 20 (2013): 5721-5726.

[217] A. N. Jain, “Surflex:  Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine,” Journal of Medicinal Chemistry 46, no. 4 (2003): 499-511.

[218] O. Korb, T. Stützle, T. E. Exner, “An Ant Colony Optimization Approach to Flexible Protein-ligand Docking,” Swarm Intelligence 1, no. 2 (2007): 115-134.

[219] Y. Feng, K. Zhang, Q. Wu, S.-Y. Huang, “NLDock: A Fast Nucleic Acid-Ligand Docking Algorithm for Modeling RNA/DNA-Ligand Complexes,” Journal of Chemical Information and Modeling 61, no. 9 (2021): 4771-4782.

[220] R. Agarwal, T. RR, J. C. Smith, “Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking,” Journal of Chemical Information and Modeling 63, no. 23 (2023): 7444-7452.

[221] S. Forli, R. Huey, M. E. Pique, M. F. Sanner, D. S. Goodsell, A. J. Olson, “Computational Protein-ligand Docking and Virtual Drug Screening With the AutoDock Suite,” Nature Protocols 11, no. 5 (2016): 905-919.

[222] G. M. Morris, R. Huey, W. Lindstrom, et al., “AutoDock4 and AutoDockTools4: Automated Docking With Selective Receptor Flexibility,” Journal of Computational Chemistry 30, no. 16 (2009): 2785-2791.

[223] H. Ma, L. Gao, Y. Jin, “RNA-ligand Interaction Scoring via Data Perturbation and Augmentation Modeling,” BioRxiv (2024). 2024.06. 26.600802.

[224] Y. Wang, Z. Chen, Z. Pan, et al., “RNAincoder: A Deep Learning-based Encoder for RNA and RNA-associated Interaction,” Nucleic Acids Research 51, no. W1 (2023): W509-W519.

[225] Z. Cai, M. Zafferani, O. M. Akande, A. E. Hargrove, “Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure,” Journal of Medicinal Chemistry 65, no. 10 (2022): 7262-7277.

[226] Z. Deng, R. Gu, and H. Bi, “Predicting Ligand - RNA Binding Using E3-Equivariant Network and Pretraining,” presented at: MLSB Workshop (NeurIPS) (2022).

[227] J. G. Carvajal-Patino, V. Mallet, D. Becerra, L. F. Nino, C. Oliver, J. Waldispuhl, “Semi-supervised Learning and Large-scale Docking Data Accelerate RNA Virtual Screening,” BioRxiv (2023). 2023.11. 23.568394.

[228] N. A. Szulc, Z. Mackiewicz, J. M. Bujnicki, F. Stefaniak, “Structural Interaction Fingerprints and Machine Learning for Predicting and Explaining Binding of Small Molecule Ligands to RNA,” Brief Bioinform 24, no. 4 (2023): bbad187.

[229] B. S. Morgan, J. E. Forte, R. N. Culver, Y. Zhang, A. E. Hargrove, “Discovery of Key Physicochemical, Structural, and Spatial Properties of RNA-Targeted Bioactive Ligands,” Angewandte Chemie International Edition 56, no. 43 (2017): 13498-13502.

[230] B. S. Morgan, J. E. Forte, A. E. Hargrove, “Insights Into the Development of Chemical Probes for RNA,” Nucleic Acids Research 46, no. 16 (2018): 8025-8037.

[231] S. L. Wicks, B. S. Morgan, A. W. Wilson, A. E. Hargrove, “Probing Bioactive Chemical Space to Discover RNA-Targeted Small Molecules,” BioRxiv (2023). 2023.07.31.551350.

[232] X. Yang, J. L. Childs-Disney, M. Paegel, M. D. Disney, “DNA-Encoded Libraries and Their Application to RNA,” Israel Journal of Chemistry 63, no. 10-11 (2023): e202300073.

[233] R. E. Kleiner, C. E. Dumelin, D. R. Liu, “Small-molecule Discovery From DNA-encoded Chemical Libraries,” Chemical Society Reviews 40, no. 12 (2011): 5707-5717.

[234] R. I. Benhamou, B. M. Suresh, Y. Tong, et al., “DNA-encoded Library versus RNA-encoded Library Selection Enables Design of an Oncogenic Noncoding RNA Inhibitor,” Proceedings of the National Academy of Sciences of the United States of America 119, no. 6 (2022): e2114971119.

[235] C. M. Connelly, F. A. Abulwerdi and J. S. Schneekloth. Discovery of RNA Binding Small Molecules Using Small Molecule Microarrays. In: Uttamchandani M, Yao SQ, eds. “Small Molecule Microarrays: Methods and Protocols” (Springer New York) (2017): 157-175.

[236] S. P. Velagapudi, M. G. Costales, B. R. Vummidi, et al., “Approved Anti-cancer Drugs Target Oncogenic Non-coding RNAs,” Cell Chemical Biology 25, no. 9 (2018): 1086-1094. e7.

[237] G. M. Keserű, D. A. Erlanson, G. G. Ferenczy, M. M. Hann, C. W. Murray, S. D. Pickett, “Design Principles for Fragment Libraries: Maximizing the Value of Learnings From Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia,” Journal of Medicinal Chemistry 59, no. 18 (2016): 8189-8206.

[238] G. Frey, E. Garcia Sega, N. Lajkiewicz, “Screening and Lead Generation Techniques for RNA Binders. RNA as a Drug Target,” Methods and Principles in Medicinal Chemistry (2024): 49-92.

[239] D. Hymon, J. Martins, C. Richter, et al., “NMR 1H,19F-based Screening of the Four Stem-looped Structure 5_SL1-SL4 Located in the 5′-untranslated Region of SARS-CoV 2 RNA,” RSC Medicinal Chemistry 15, no. 1 (2024): 165-177.

[240] H. S. Haniff, Y. Tong, X. Liu, et al., “Targeting the SARS-CoV-2 RNA Genome With Small Molecule Binders and Ribonuclease Targeting Chimera (RIBOTAC) Degraders,” ACS Central Science 6, no. 10 (2020): 1713-1721.

[241] S. Mikutis, M. Rebelo, E. Yankova, et al., “Proximity-induced Nucleic Acid Degrader (PINAD) Approach to Targeted RNA Degradation Using Small Molecules,” ACS Central Science 9, no. 5 (2023): 892-904.

[242] J. Chen, J. Stubbe, “Bleomycins: Towards Better Therapeutics,” Nature Reviews Cancer 5, no. 2 (2005): 102-12.

[243] S. P. Velagapudi, M. D. Cameron, C. L. Haga, et al., “Design of a Small Molecule Against an Oncogenic Noncoding RNA,” PNAS 113, no. 21 (2016): 5898-903.

[244] Y. Li, M. D. Disney, “Precise Small Molecule Degradation of a Noncoding RNA Identifies Cellular Binding Sites and Modulates an Oncogenic Phenotype,” Acs Chemical Biology 13, no. 11 (2018): 3065-3071.

[245] A. J. Angelbello, M. D. Disney, “Bleomycin Can Cleave an Oncogenic Noncoding RNA,” Chembiochem 19, no. 1 (2018): 43-47.

[246] S. G. Rzuczek, L. A. Colgan, Y. Nakai, et al., “Precise Small-molecule Recognition of a Toxic CUG RNA Repeat Expansion,” Nature Chemical Biology 13, no. 2 (2017): 188-193.

[247] A. J. Angelbello, S. G. Rzuczek, K. K. McKee, et al., “Precise Small-molecule Cleavage of an R(CUG) Repeat Expansion in a Myotonic Dystrophy Mouse Model,” PNAS 116, no. 16 (2019): 7799-7804.

[248] A. J. Angelbello, M. E. DeFeo, C. M. Glinkerman, D. L. Boger, M. D. Disney, “Precise Targeted Cleavage of a R(CUG) Repeat Expansion in Cells by Using a Small-Molecule-Deglycobleomycin Conjugate,” Acs Chemical Biology 15, no. 4 (2020): 849-855.

[249] R. I. Benhamou, M. Abe, S. Choudhary, S. M. Meyer, A. J. Angelbello, M. D. Disney, “Optimization of the Linker Domain in a Dimeric Compound That Degrades an R (CUG) Repeat Expansion in Cells,” Journal of Medicinal Chemistry 63, no. 14 (2020): 7827-7839.

[250] C. S. Chow, J. K. Barton, “Transition-Metal Complexes as Probes of Nucleic-Acids,” Methods in Enzymology 212 (1992): 219-242.

[251] B. Armitage, “Photocleavage of Nucleic Acids,” Chemical Reviews 98, no. 3 (1998): 1171-1200.

[252] H. Lonnberg, “Cleavage of RNA Phosphodiester Bonds by Small Molecular Entities: A Mechanistic Insight,” Organic & Biomolecular Chemistry 9, no. 6 (2011): 1687-703.

[253] J. R. Morrow, O. Iranzo, “Synthetic Metallonucleases for RNA Cleavage,” Current Opinion in Chemical Biology 8, no. 2 (2004): 192-200.

[254] T. Niittymaki, H. Lonnberg, “Artificial Ribonucleases,” Organic & Biomolecular Chemistry 4, no. 1 (2006): 15-25.

[255] X. Li, Y. Song, “Proteolysis-targeting Chimera (PROTAC) for Targeted Protein Degradation and Cancer Therapy,” Journal of hematology & oncology 13, no. 1 (2020): 50.

[256] M. Bekes, D. R. Langley, C. M. Crews, “PROTAC Targeted Protein Degraders: The Past Is Prologue,” Nature Reviews Drug Discovery 21, no. 3 (2022): 181-200.

[257] M. G. Costales, Y. Matsumoto, S. P. Velagapudi, M. D. Disney, “Small Molecule Targeted Recruitment of a Nuclease to RNA,” Journal of the American Chemical Society 140, no. 22 (2018): 6741-6744.

[258] M. G. Costales, B. Suresh, K. Vishnu, M. D. Disney, “Targeted Degradation of a Hypoxia-Associated Non-coding RNA Enhances the Selectivity of a Small Molecule Interacting With RNA,” Cell Chemical Biology 26, no. 8 (2019): 1180-1186. e5.

[259] M. G. Costales, H. Aikawa, Y. Li, et al., “Small-molecule Targeted Recruitment of a Nuclease to Cleave an Oncogenic RNA in a Mouse Model of Metastatic Cancer,” Proceedings of the National Academy of Sciences of the United States of America 117, no. 5 (2020): 2406-2411.

[260] C. S. Thakur, B. K. Jha, B. Dong, et al., “Small-molecule Activators of RNase L With Broad-spectrum Antiviral Activity,” PNAS 104, no. 23 (2007): 9585-90.

[261] P. Zhang, X. Liu, D. Abegg, et al., “Reprogramming of Protein-Targeted Small-Molecule Medicines to RNA by Ribonuclease Recruitment,” Journal of the American Chemical Society 143, no. 33 (2021): 13044-13055.

[262] J. A. Bush, H. Aikawa, R. Fuerst, et al., “Ribonuclease Recruitment Using a Small Molecule Reduced c9ALS/FTD R(G(4)C(2)) Repeat Expansion in Vitro and in Vivo ALS Models,” Science Translational Medicine 13, no. 617 (2021): eabd5991.

[263] Y. Zhang, L. Wang, F. Wang, X. Chu, J.-H. Jiang, “Quadruplex mRNAs Silencing With Inducible Ribonuclease Targeting Chimera for Precision Tumor Therapy,” Journal of the American Chemical Society 146, no. 23 (2024): 15815-15824.

[264] C. Zhao, G. Qin, J. Niu, et al., “Targeting RNA G-Quadruplex in SARS-CoV-2: A Promising Therapeutic Target for COVID-19?,” Angewandte Chemie (International ed in English) 60, no. 1 (2021): 432-438.

[265] J. A. Kelly, A. N. Olson, K. Neupane, et al., “Structural and Functional Conservation of the Programmed -1 Ribosomal Frameshift Signal of SARS Coronavirus 2 (SARS-CoV-2),” Journal of Biological Chemistry 295, no. 31 (2020): 10741-10748.

[266] M. Corley, M. C. Burns, G. W. Yeo, “How RNA-Binding Proteins Interact With RNA: Molecules and Mechanisms,” Molecular Cell 78, no. 1 (2020): 9-29.

[267] A. Busch, K. J. Hertel, “Evolution of SR Protein and hnRNP Splicing Regulatory Factors,” Wires Rna 3, no. 1 (2012): 1-12.

[268] J. B. Querido, I. Díaz-López, V. Ramakrishnan, “The Molecular Basis of Translation Initiation and Its Regulation in Eukaryotes,” Nature Reviews Molecular Cell Biology 25, no. 3 (2024): 168-186.

[269] G. Meister, “Argonaute Proteins: Functional Insights and Emerging Roles,” Nature Reviews Genetics 14, no. 7 (2013): 447-459.

[270] D. Arseni, M. Hasegawa, A. G. Murzin, et al., “Structure of Pathological TDP-43 filaments From ALS With FTLD,” Nature 601, no. 7891 (2022): 139-143.

[271] J. M. Minkoff, B. tenOever, “Innate Immune Evasion Strategies of SARS-CoV-2,” Nature Reviews Microbiology 21, no. 3 (2023): 178-194.

[272] S. J. Zhang, W. Z. Huang, L. L. Ren, et al., “Comparison of Viral RNA-host Protein Interactomes Across Pathogenic RNA Viruses Informs Rapid Antiviral Drug Discovery for SARS-CoV-2,” Cell Research 32, no. 1 (2022): 9-23.

[273] M. W. Hentze, A. Castello, T. Schwarzl, T. Preiss, “A Brave New World of RNA-binding Proteins,” Nature Reviews Molecular Cell Biology 19, no. 5 (2018): 327-341.

[274] A. Castello, B. Fischer, C. K. Frese, et al., “Comprehensive Identification of RNA-Binding Domains in Human Cells,” Molecular Cell 63, no. 4 (2016): 696-710.

[275] M. Ramanathan, D. F. Porter, P. A. Khavari, “Methods to Study RNA-protein Interactions,” Nature Methods 16, no. 3 (2019): 225-234.

[276] F. C. Y. Lee, J. Ule, “Advances in CLIP Technologies for Studies of Protein-RNA Interactions,” Molecular Cell 69, no. 3 (2018): 354-369.

[277] J. K. Wei, S. Y. Chen, L. C. Zong, X. Gao, Y. Li, “Protein-RNA Interaction Prediction With Deep Learning: Structure Matters,” Brief Bioinform 23, no. 1 (2022): bbab540.

[278] H. L. Ma, H. Wen, Z. Y. Xue, G. J. Li, Z. L. Zhang, “RNANetMotif: Identifying Sequence-structure RNA Network Motifs in RNA-protein Binding Sites,” Plos Computational Biology 18, no. 7 (2022): e1010293.

[279] B. Alipanahi, A. Delong, M. T. Weirauch, B. J. Frey, “Predicting the Sequence Specificities of DNA- and RNA-binding Proteins by Deep Learning,” Nature Biotechnology 33, no. 8 (2015): 831-838.

[280] Y. M. Yan, D. Zhang, P. Zhou, B. T. Li, S. Y. Huang, “HDOCK: A Web Server for Protein-protein and Protein-DNA/RNA Docking Based on a Hybrid Strategy,” Nucleic Acids Research 45, no. W1 (2017): W365-W373.

[281] D. Maticzka, S. J. Lange, F. Costa, R. Backofen, “GraphProt: Modeling Binding Preferences of RNA-binding Proteins,” Genome biology 15, no. 1 (2014): R17.

[282] J. Fiorentino, A. Armaos, A. Colantoni, G. G. Tartaglia, “Prediction of Protein-RNA Interactions From Single-cell Transcriptomic Data,” Nucleic Acids Research 52, no. 6 (2024): e31.

[283] W. G. Byun, D. Lim, S. B. Park, “Small-molecule Modulators of Protein-RNA Interactions,” Current Opinion in Chemical Biology 68 (2022): 102149.

[284] P. Wu, “Inhibition of RNA-binding Proteins With Small Molecules,” Nature Reviews Chemistry 4, no. 9 (2020): 441-458.

[285] B. Hook, D. Bernstein, B. Zhang, M. Wickens, “RNA-protein Interactions in the Yeast Three-hybrid System: Affinity, Sensitivity, and Enhanced Library Screening,” Rna 11, no. 2 (2005): 227-33.

[286] D. Lim, W. G. Byun, J. Y. Koo, H. Park, S. B. Park, “Discovery of a Small-Molecule Inhibitor of Protein-MicroRNA Interaction Using Binding Assay With a Site-Specifically Labeled Lin28,” Journal of the American Chemical Society 138, no. 41 (2016): 13630-13638.

[287] J. Chu, W. Zhang, R. Cencic, et al., “Amidino-Rocaglates: A Potent Class of eIF4A Inhibitors,” Cell Chemical Biology 26, no. 11 (2019): 1586-1593. e3.

[288] M. F. S. Degenhardt, H. F. Degenhardt, Y. R. Bhandari, et al., “Determining Structures of RNA Conformers Using AFM and Deep Neural Networks,” Nature 637, no. 8048 (2025): 1234-1243.

[289] Y.-T. Lee, M. F. S. Degenhardt, I. Skeparnias, et al., “The Conformational Space of RNase P RNA in Solution,” Nature 637, no. 8048 (2025): 1244-1251.

RIGHTS & PERMISSIONS

2025 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.