RNA-targeted small-molecule drug discoveries: a machine-learning perspective

References

• Warner KD, Hajdin CE, Weeks KM. Principles for targeting RNA with drug-like small molecules. Nat Rev Drug Discov. 2018;17(8):547–558. doi: 10.1038/nrd.2018.93
   PubMed Web of Science ®Google Scholar
• Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74.
   PubMed Web of Science ®Google Scholar
• Hangauer MJ, Vaughn IW, McManus MT, et al. Pervasive transcription of the human genome produces thousands of previously unidentified long intergenic noncoding RNAs. PLoS Genet. 2013;9(6):e1003569. doi: 10.1371/journal.pgen.1003569
   PubMed Web of Science ®Google Scholar
• Djebali S, Davis CA, Merkel A, et al. Landscape of transcription in human cells. Nature. 2012;489(7414):101–108. doi: 10.1038/nature11233
   PubMed Web of Science ®Google Scholar
• Haga CL, Phinney DG. Strategies for targeting RNA with small molecule drugs. Expert Opin Drug Discov. 2022;18(2):1–13.
   PubMed Web of Science ®Google Scholar
• Garner AL. Contemporary progress and opportunities in RNA-Targeted drug discovery. ACS Med Chem Lett. 2023;14(3):251–259. doi: 10.1021/acsmedchemlett.3c00020
   PubMed Web of Science ®Google Scholar
• Rabie AM, Abdalla M. Forodesine and riboprine exhibit strong anti-SARS-CoV-2 repurposing potential: in Silico and in vitro studies. ACS Bio & Med Chem Au. 2022;2(6):565–585. doi: 10.1021/acsbiomedchemau.2c00039
   PubMedGoogle Scholar
• Eltayb WA, Abdalla M, Rabie AM. Novel investigational anti-SARS-CoV-2 agent ensitrelvir “S-217622”: a very promising potential universal broad-spectrum antiviral at the therapeutic frontline of coronavirus species. ACS Omega. 2023;8(6):5234–5246. doi: 10.1021/acsomega.2c03881
   PubMedGoogle Scholar
• Rabie AM, Eltayb WA. Potent dual polymerase/exonuclease inhibitory activities of antioxidant aminothiadiazoles against the COVID-19 omicron virus: a promising in silico/in vitro repositioning research study. Heidelberger, Berlin: Mol Biotechnol; 2023.
   Google Scholar
• Crooke ST, Witztum JL, Bennett CF, et al. RNA-Targeted therapeutics. Cell Metab. 2018;27(4):714–739. doi: 10.1016/j.cmet.2018.03.004
   PubMed Web of Science ®Google Scholar
• Corey DR. Nusinersen, an antisense oligonucleotide drug for spinal muscular atrophy. Nat Neurosci. 2017;20(4):497–499. doi: 10.1038/nn.4508
   PubMed Web of Science ®Google Scholar
• Kristen AV, Ajroud-Driss S, Conceicao I, et al. Patisiran, an RNAi therapeutic for the treatment of hereditary transthyretin-mediated amyloidosis. Neurodegener Dis Manag. 2019;9(1):5–23. doi: 10.2217/nmt-2018-0033
   PubMed Web of Science ®Google Scholar
• Manigrasso J, Marcia M, De Vivo M. Computer-aided design of RNA-targeted small molecules: a growing need in drug discovery. Chem. 2021;7(11):2965–2988. doi: 10.1016/j.chempr.2021.05.021
   Web of Science ®Google Scholar
• Costales MG, Childs-Disney JL, Haniff HS, et al. How We think about targeting RNA with small molecules. J Med Chem. 2020;63(17):8880–8900. doi: 10.1021/acs.jmedchem.9b01927
   PubMed Web of Science ®Google Scholar
• Childs-Disney JL, Yang X, Gibaut QMR, et al. Targeting RNA structures with small molecules. Nat Rev Drug Discov. 2022;21:736–762. doi: 10.1038/s41573-022-00521-4
   PubMed Web of Science ®Google Scholar
• Seetin MG, Mathews DH. RNA structure prediction: an overview of methods. Methods Mol Biol. 2012;905:99–122.
   PubMedGoogle Scholar
• Weeks KM. Advances in RNA structure analysis by chemical probing. Curr Opin Struct Biol. 2010;20(3):295–304. doi: 10.1016/j.sbi.2010.04.001
   PubMed Web of Science ®Google Scholar
• Mortimer SA, Kidwell MA, Doudna JA. Insights into RNA structure and function from genome-wide studies. Nat Rev Genet. 2014;15(7):469–479. doi: 10.1038/nrg3681
   PubMed Web of Science ®Google Scholar
• Wang X-W, Liu C-X, Chen L-L, et al. RNA structure probing uncovers RNA structure-dependent biological functions. Nat Chem Biol. 2021;17(7):755–766. doi: 10.1038/s41589-021-00805-7
   PubMed Web of Science ®Google Scholar
• Ramaswamy A, Balasubramanian S, Rajagopalan M. Chapter 2 - biomolecular talks—part 1: a theoretical revisit on molecular modeling and docking approaches. In: Coumar MSeditor Molecular docking for computer-aided drug design.Academic Press; 2021. pp. 31–55. doi: 10.1016/B978-0-12-822312-3.00015-1
   Google Scholar
• Bibi S, Ul Islam F, Olawale OA, et al. Chapter 20 - in silico analysis for such natural compounds and COVID-19. In: Niaz KeditorApplication of natural products in SARS-CoV-2.Academic Press; 2023. pp. 463–489. doi: 10.1016/B978-0-323-95047-3.00019-8
   Google Scholar
• Del Rio A, Varchi G. Chapter 9 - molecular design of compounds targeting histone methyltransferases. Epi-Informatics. J.L. Medina-Franco Editor. Boston: Academic Press;pp. 257–272. 2016. doi: 10.1016/B978-0-12-802808-7.00009-5
   Google Scholar
• Greener JG, Kandathil SM, Moffat L, et al. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40–55. doi: 10.1038/s41580-021-00407-0
   PubMed Web of Science ®Google Scholar
• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi: 10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Takefuji Y, Chen LL, Lee KC, et al. Parallel algorithms for finding a near-maximum independent set of a circle graph. IEEE Trans Neural Networks. 1990;1(3):263–267. doi: 10.1109/72.80251
   PubMedGoogle Scholar
• Sakakibara Y, Brown M, Hughey R, et al. Stochastic context-free grammers for tRNA modeling. Nucleic Acids Res. 1994;22(23):5112–5120. doi: 10.1093/nar/22.23.5112
   PubMed Web of Science ®Google Scholar
• Xia T, SantaLucia J Jr., Burkard ME, et al. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson−crick base pairs. Biochemistry. 1998;37(42):14719–14735. doi: 10.1021/bi9809425
   PubMed Web of Science ®Google Scholar
• Hor CY, Yang CB, Chang CH, et al. A tool preference choice method for RNA secondary structure prediction by SVM with statistical tests. Vol. 9. Auckland, New Zealand: Evol Bioinform Online; 2013. pp. 163–184.
   Google Scholar
• Sample PJ, Wang B, Reid DW, et al. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay. Nat Biotechnol. 2019;37(7):803–809. doi: 10.1038/s41587-019-0164-5
   PubMed Web of Science ®Google Scholar
• Singh J, Hanson J, Paliwal K, et al. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat Commun. 2019;10(1):5407. doi: 10.1038/s41467-019-13395-9
   PubMedGoogle Scholar
• Lei X, Mudiyanselage TB, Zhang Y, et al. A comprehensive survey on computational methods of non-coding RNA and disease association prediction. Brief Bioinform. 2021;22(4):bbaa350. doi: 10.1093/bib/bbaa350
   PubMed Web of Science ®Google Scholar
• Shao Y, Zhang QC. Targeting RNA structures in diseases with small molecules. Essays Biochem. 2020;64(6):955–966. doi: 10.1042/EBC20200011
   PubMed Web of Science ®Google Scholar
• Wilkinson KA, Merino EJ, Weeks KM. Selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution. Nat Protoc. 2006;1(3):1610–1616. doi: 10.1038/nprot.2006.249
   PubMed Web of Science ®Google Scholar
• Tijerina P, Mohr S, Russell R. DMS footprinting of structured RNAs and RNA-protein complexes. Nat Protoc. 2007;2(10):2608–2623. doi: 10.1038/nprot.2007.380
   PubMed Web of Science ®Google Scholar
• Ackermann D, Famulok M. Pseudo-complementary PNA actuators as reversible switches in dynamic DNA nanotechnology. Nucleic Acids Res. 2013;41(8):4729–4739. doi:10.1093/nar/gkt121.
   PubMed Web of Science ®Google Scholar
• Smyth MS, Martin JH. X ray crystallography. Mol Pathol. 2000;53(1):8–14. doi: 10.1136/mp.53.1.8
   PubMedGoogle Scholar
• Doerr A. Cryo-electron tomography. Nat Methods. 2017;14(1):34–34. doi: 10.1038/nmeth.4115
   Web of Science ®Google Scholar
• Padroni G, Eubanks CS, Hargrove AE. Differentiation and classification of RNA motifs using small molecule-based pattern recognition. Methods Enzymol. 2019;623:101–130.
   PubMed Web of Science ®Google Scholar
• Westhof E, Auffinger P. RNA tertiary structure, in encyclopedia of analytical chemistry. Hoboken, New Jersey, U.S: John Wiley & Sons; 2006.
   Google Scholar
• Quigley GJ, Rich A. Structural domains of transfer RNA molecules. Science. 1976;194(4267):796–806. doi: 10.1126/science.790568
   PubMed Web of Science ®Google Scholar
• Nissen P, Ippolito JA, Ban N, et al. RNA tertiary interactions in the large ribosomal subunit: the A-minor motif. Proc Natl Acad Sci U S A. 2001;98(9):4899–4903. doi: 10.1073/pnas.081082398
   PubMed Web of Science ®Google Scholar
• Batey RT, Rambo RP, Doudna JA. Tertiary Motifs in RNA structure and folding. Angew Chem Int Ed Engl. 1999;38(16):2326–2343. doi: 10.1002/(SICI)1521-3773(19990816)38:16<2326:AID-ANIE2326>3.0.CO;2-3
   PubMed Web of Science ®Google Scholar
• Zuker M, Stiegler P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 1981;9(1):133–148. doi: 10.1093/nar/9.1.133
   PubMed Web of Science ®Google Scholar
• Hofacker IL, Fontana W, Stadler PF, et al. Fast folding and comparison of RNA secondary structures. Monatshefte für Chemie/Chem Monthly. 1994;125(2):167–188. doi: 10.1007/BF00818163
   Google Scholar
• Theis C, Janssen S, Giegerich R. 2010. Prediction of RNA secondary structure including kissing hairpin motifs. Algorithms bioinform. BerlinBerlin Heidelberg: Springer; pp. 52–64. doi: 10.1007/978-3-642-15294-8_5
   Google Scholar
• Reeder J, Steffen P, Giegerich R. pknotsRG: rNA pseudoknot folding including near-optimal structures and sliding windows. Nucleic Acids Res. 2007;35(suppl_2):W320–W324. doi: 10.1093/nar/gkm258
   PubMedGoogle Scholar
• Rivas E, Eddy SR. A dynamic programming algorithm for RNA structure prediction including pseudoknots11Edited by I. Tinoco J Mol Biol. 1999;285(5):2053–2068. doi: 10.1006/jmbi.1998.2436
   PubMed Web of Science ®Google Scholar
• Tsang HH, Wiese KC. SARNA-Predict: accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing. IEEE/ACM Trans Comput Biol Bioinform. 2010;7(4):727–740. doi: 10.1109/TCBB.2008.97
   PubMed Web of Science ®Google Scholar
• Ding Y, Lawrence CE. A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res. 2003;31(24):7280–7301. doi: 10.1093/nar/gkg938
   PubMed Web of Science ®Google Scholar
• Ding Y, Chan CY, Lawrence CE. Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res. 2004;32(suppl_2):W135–W141. doi: 10.1093/nar/gkh449
   PubMedGoogle Scholar
• Dawson W, Takai T, Ito N, et al. A new entropy model for RNA: part III. Is the folding free energy landscape of RNA funnel shaped? J Nucleic Acids Investig. 2014;5(1). doi: 10.4081/jnai.2014.2652
   Google Scholar
• Dawson WK, Fujiwara K, Kawai G, et al. Prediction of RNA pseudoknots using heuristic modeling with mapping and sequential folding. PLoS ONE. 2007;2(9):e905. doi: 10.1371/journal.pone.0000905
   PubMed Web of Science ®Google Scholar
• Bindewald E, Shapiro BA. RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA. 2006;12(3):342–352. doi: 10.1261/rna.2164906
   PubMed Web of Science ®Google Scholar
• Liu Q, Ye X, Zhang Y A hopfield neural network based algorithm for RNA secondary structure prediction. In First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS’06); 2006; Hangzhou, China.
   Google Scholar
• Wu H, Tang Y, Lu W, et al. 2018. RNA secondary structure prediction based on long short-term memory model. Intelligent computing theories and application. Cham: Springer International Publishing; pp. 595–599. doi: 10.1007/978-3-319-95930-6_59
   Google Scholar
• Lu W, Tang Y, Wu H, et al. Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter. BMC Bioinf. 2019;20(25):684. doi: 10.1186/s12859-019-3258-7
   PubMedGoogle Scholar
• Wang L, Liu Y, Zhong X, et al. Dmfold: a novel method to predict RNA secondary structure with pseudoknots based on deep learning and improved base pair maximization principle. Front Genet. 2019;10:10. doi: 10.3389/fgene.2019.00143
   PubMed Web of Science ®Google Scholar
• Singh J, Paliwal K, Zhang T, et al. Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning. Bioinformatics. 2021;37(17):2589–2600. doi: 10.1093/bioinformatics/btab165
   PubMed Web of Science ®Google Scholar
• Zhang H, Zhang C, Li Z., et al.Machine Learning Techniques on Gene Function Prediction. In Sangaiah, AK, editor . A new method of RNA secondary structure prediction based on convolutional neural network and dynamic programming. Lausanne, Switzerland: Front Genet; 2019. p. 10.
   Google Scholar
• Chen X, Li Y, Umarov R, et al., RNA secondary structure prediction by learning unrolled algorithms. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net.
   Google Scholar
• Zhao Q, Zhao Z, Fan X, et al. Review of machine learning methods for RNA secondary structure prediction. PLoS Comput Biol. 2021;17(8):e1009291. doi: 10.1371/journal.pcbi.1009291
   PubMed Web of Science ®Google Scholar
• Calonaci N, Jones A, Cuturello F, et al. Machine learning a model for RNA structure prediction. NAR Genom Bioinform. 2020;2(4). doi: 10.1093/nargab/lqaa090
   PubMedGoogle Scholar
• RNAfold. 2022 October 18. Available from: http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi
   Google Scholar
• Sato K, Akiyama M, Sakakibara Y. RNA secondary structure prediction using deep learning with thermodynamic integration. Nat Commun. 2021;12(1):941. doi: 10.1038/s41467-021-21194-4
   PubMed Web of Science ®Google Scholar
• Willmott D, Murrugarra D, Ye Q. Improving RNA secondary structure prediction via state inference with deep recurrent neural networks. Comput Math Biophys. 2020;8(1):36–50. doi: 10.1515/cmb-2020-0002
   Google Scholar
• Deigan KE, Li TW, Mathews DH, et al. Accurate SHAPE-directed RNA structure determination. Proc Natl Acad Sci U S A. 2009;106(1):97–102. doi: 10.1073/pnas.0806929106
   PubMed Web of Science ®Google Scholar
• Quan L, Cai L, Chen Y, et al. Developing parallel ant colonies filtered by deep learned constrains for predicting RNA secondary structure with pseudo-knots. Neurocomputing. 2020;384:104–114. doi: 10.1016/j.neucom.2019.12.041
   Web of Science ®Google Scholar
• Huang B, Du Y, Zhang S, et al. Computational prediction of RNA tertiary structures using machine learning methods*. Chin Phys B. 2020;29(10):108704. doi: 10.1088/1674-1056/abb303
   Web of Science ®Google Scholar
• Frellsen J, Moltke I, Thiim M, et al. A probabilistic model of RNA conformational space. PLoS Comput Biol. 2009;5(6):e1000406. doi: 10.1371/journal.pcbi.1000406
   PubMed Web of Science ®Google Scholar
• Wang Z, Xu J. A conditional random fields method for RNA sequence–structure relationship modeling and conformation sampling. Bioinformatics. 2011;27(13):i102–i110. doi: 10.1093/bioinformatics/btr232
   PubMed Web of Science ®Google Scholar
• Cruz JA, Westhof E. Sequence-based identification of 3D structural modules in RNA with RMDetect. Nat Methods. 2011;8(6):513–519. doi: 10.1038/nmeth.1603
   PubMed Web of Science ®Google Scholar
• Theis C, Höner Zu Siederdissen C, Hofacker IL, et al. Automated identification of RNA 3D modules with discriminative power in RNA structural alignments. Nucleic Acids Res. 2013;41(22):9999–10009. doi: 10.1093/nar/gkt795
   PubMed Web of Science ®Google Scholar
• Zirbel CL, Roll J, Sweeney BA, et al. Identifying novel sequence variants of RNA 3D motifs. Nucleic Acids Res. 2015;43(15):7504–7520. doi: 10.1093/nar/gkv651
   PubMed Web of Science ®Google Scholar
• Li J, Zhu W, Wang J, et al. RNA3DCNN: local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks. PLoS Comput Biol. 2018;14(11):e1006514. doi: 10.1371/journal.pcbi.1006514
   PubMed Web of Science ®Google Scholar
• Wang YZ, Li J, Zhang S, et al. An RNA scoring function for tertiary structure prediction based on multi-layer neural networks. Mol Biol. 2019;53(1):118–126. doi: 10.1134/S0026893319010175
   Web of Science ®Google Scholar
• Townshend RJL, Eismann S, Watkins AM, et al. Geometric deep learning of RNA structure. Science. 2021;373(6558):1047–1051. doi: 10.1126/science.abe5650
   PubMed Web of Science ®Google Scholar
• Deng C, Tang Y, Zhang J, et al. RNAGCN: rNA tertiary structure assessment with a graph convolutional network. Chin Phys B. 2022;31(11):118702. doi: 10.1088/1674-1056/ac8ce3
   Web of Science ®Google Scholar
• Comeau SR, Gatchell DW, Vajda S, et al. ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res. 2004;32(Web Server issue):W96–9. doi: 10.1093/nar/gkh354
   PubMedGoogle Scholar
• Schneidman-Duhovny D, Inbar Y, Nussinov R, et al. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005;33(Web Server issue):W363–7. doi: 10.1093/nar/gki481
   PubMed Web of Science ®Google Scholar
• Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein-protein docking. Nucleic Acids Res. 2006;34(Web Server issue):W310–4. doi:10.1093/nar/gkl206
   PubMedGoogle Scholar
• Kozakov D, Brenke R, Comeau SR, et al. PIPER: an FFT-based protein docking program with pairwise potentials. Proteins. 2006;65(2):392–406. doi: 10.1002/prot.21117
   PubMed Web of Science ®Google Scholar
• Lyskov S, Gray JJ. The RosettaDock server for local protein-protein docking. Nucleic Acids Res. 2008;36(Web Server issue):W233–8. doi: 10.1093/nar/gkn216
   PubMedGoogle Scholar
• Morley SD, Afshar M. Validation of an empirical RNA-ligand scoring function for fast flexible docking using Ribodock. J Comput Aided Mol Des. 2004;18(3):189–208. doi: 10.1023/B:JCAM.0000035199.48747.1e
   PubMed Web of Science ®Google Scholar
• Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, et al. rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol. 2014;10(4):e1003571. doi: 10.1371/journal.pcbi.1003571
   PubMed Web of Science ®Google Scholar
• Chhabra S, Xie J, Frank AT. Rnaposers: machine learning classifiers for ribonucleic acid-ligand poses. J Phys Chem B. 2020;124(22):4436–4445. doi: 10.1021/acs.jpcb.0c02322
   PubMed Web of Science ®Google Scholar
• Stefaniak F, Bujnicki JM, Schlick T. AnnapuRNA: a scoring function for predicting RNA-small molecule binding poses. PLoS Comput Biol. 2021;17(2):e1008309. doi: 10.1371/journal.pcbi.1008309
   PubMed Web of Science ®Google Scholar
• Winkle M, El-Daly SM, Fabbri M, et al. Noncoding RNA therapeutics - challenges and potential solutions. Nat Rev Drug Discov. 2021;20(8):629–651. doi: 10.1038/s41573-021-00219-z
   PubMed Web of Science ®Google Scholar
• Matsui M, Corey DR. Non-coding RNAs as drug targets. Nat Rev Drug Discov. 2017;16(3):167–179. doi: 10.1038/nrd.2016.117
   PubMed Web of Science ®Google Scholar
• Liu X, Wang S, Meng F, et al. Sm2mir: a database of the experimentally validated small molecules’ effects on microRNA expression. Bioinformatics. 2013;29(3):409–411. doi: 10.1093/bioinformatics/bts698
   PubMed Web of Science ®Google Scholar
• Meng F, Wang J, Dai E, et al. Psmir: a database of potential associations between small molecules and miRnas. Sci Rep. 2016;6(1):19264. doi: 10.1038/srep19264
   PubMedGoogle Scholar
• Chang L, Zhou G, Soufan O, et al. miRnet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020;48(W1):W244–W251. doi: 10.1093/nar/gkaa467
   PubMed Web of Science ®Google Scholar
• Shen C, Luo J, Lai Z, et al. Multiview joint learning-based method for identifying small-molecule-associated MiRNAs by Integrating pharmacological, genomics, and network knowledge. J Chem Inf Model. 2020;60(8):4085–4097. doi: 10.1021/acs.jcim.0c00244
   PubMed Web of Science ®Google Scholar
• Zhuo Z, Wan Y, Guan D, et al. A loop-based and AGO-Incorporated virtual screening model targeting AGO-Mediated miRNA-Mrna interactions for drug discovery to rescue bone phenotype in genetically modified mice. Adv Sci. 2020;7(13):1903451. doi: 10.1002/advs.201903451
   Web of Science ®Google Scholar
• Wang CC, Chen X, Qu J, et al. RFSMMA: a new computational model to identify and prioritize potential small molecule-MiRNA associations. J Chem Inf Model. 2019;59(4):1668–1679. doi: 10.1021/acs.jcim.9b00129
   PubMed Web of Science ®Google Scholar
• Zhao Y, Chen X, Yin J, et al. SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association. RNA Biol. 2020;17(2):281–291. doi: 10.1080/15476286.2019.1694732
   PubMed Web of Science ®Google Scholar
• Xie WB, Yan H, Zhao XM. EmDL: extracting miRNA-Drug Interactions from Literature. IEEE/ACM Trans Comput Biol Bioinform. 2019;16(5):1722–1728. doi: 10.1109/TCBB.2017.2723394
   PubMedGoogle Scholar
• Oliver C, Mallet V, Gendron RS, et al. Augmented base pairing networks encode RNA-small molecule binding preferences. Nucleic Acids Res. 2020;48(14):7690–7699. doi:10.1093/nar/gkaa583
   PubMed Web of Science ®Google Scholar
• Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235–242. doi: 10.1093/nar/28.1.235
   PubMed Web of Science ®Google Scholar
• Kozlovskii I, Popov P. Structure-based deep learning for binding site detection in nucleic acid macromolecules. NAR Genom Bioinform. 2021;3(4):lqab111. doi: 10.1093/nargab/lqab111
   PubMedGoogle Scholar
• Wang K, Zhou R, Wu Y, et al. Rlbind: a deep learning method to predict RNA–ligand binding sites. Brief Bioinform. 2022;24(1). doi: 10.1093/bib/bbac486
   Web of Science ®Google Scholar
• Yazdani K, Jordan D, Yang M, et al. Machine learning informs RNA-Binding chemical space. Angew Chem Int Ed Engl. 2023;62(11):e202211358. doi: 10.1002/anie.202211358
   PubMed Web of Science ®Google Scholar
• Jamal S, Periwal V, Open Source Drug Discovery C, et al. Computational analysis and predictive modeling of small molecule modulators of microRNA. J Cheminform. 2012;4(1):16. doi: 10.1186/1758-2946-4-16
   PubMedGoogle Scholar
• Cai Z, Zafferani M, Akande OM, et al. Quantitative structure-activity relationship (QSAR) study predicts small-molecule binding to RNA structure. J Med Chem. 2022;65(10):7262–7277. doi: 10.1021/acs.jmedchem.2c00254
   PubMed Web of Science ®Google Scholar
• Rizvi NF, Santa Maria JP Jr., Nahvi A, et al. Targeting RNA with small molecules: identification of selective, RNA-Binding small molecules occupying drug-like chemical space. SLAS Discov. 2020;25(4):384–396. doi: 10.1177/2472555219885373
   PubMed Web of Science ®Google Scholar
• Grimberg H, Tiwari VS, Tam B, et al. Machine learning approaches to optimize small-molecule inhibitors for RNA targeting. J Cheminform. 2022;14(1):4. doi: 10.1186/s13321-022-00583-x
   PubMed Web of Science ®Google Scholar
• Cho K, Merrienboer Bv., Bahdanau D, and Bengio Y On the properties of neural machine translation: encoder–decoder approaches in SSST@EMNLP 2014.
   Google Scholar
• Cho K, Merrienboer BV, Gülçehre Ç, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP); Doha, Qatar: Association for Computational Linguistics; 2014. p. 1724–1734.
   Google Scholar
• Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst. 2014;2:27.
   Google Scholar
• Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 3rd International Conference on Learning Representations; 2015 May 7–9; San Diego, CA, USA; 2015.
   Google Scholar
• Vaswani A, Shazeer N, Parmar N, et al., Attention is all you need. in Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, Curran Associates Inc.: Long Beach, California, USA. p. 6000–6010.
   Google Scholar
• Falese JP, Donlic A, Hargrove AE. Targeting RNA with small molecules: from fundamental principles towards the clinic. Chem Soc Rev. 2021;50(4):2224–2243. doi: 10.1039/D0CS01261K
   PubMed Web of Science ®Google Scholar
• Disney MD, Winkelsas AM, Velagapudi SP, et al. Inforna 2.0: a platform for the sequence-based design of small molecules targeting structured RNAs. ACS Chem Biol. 2016;11(6):1720–1728. doi: 10.1021/acschembio.6b00001
   PubMed Web of Science ®Google Scholar
• Sun S, Yang J, Zhang Z. Rnaligands: a database and web server for RNA-ligand interactions. RNA. 2022;28(2):115–122. doi: 10.1261/rna.078889.121
   PubMed Web of Science ®Google Scholar
• Ghislat G, Rahman T, Ballester PJ. Recent progress on the prospective application of machine learning to structure-based virtual screening. Curr Opin Chem Biol. 2021;65:28–34. doi: 10.1016/j.cbpa.2021.04.009
   PubMed Web of Science ®Google Scholar
• AlQuraishi M. Machine learning in protein structure prediction. Curr Opin Chem Biol. 2021;65:1–8. doi: 10.1016/j.cbpa.2021.04.005
   PubMed Web of Science ®Google Scholar
• Mouchlis VD, Afantitis A, Serra A, et al. Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci. 2021;22(4):1676. doi: 10.3390/ijms22041676
   PubMed Web of Science ®Google Scholar
• Bagnolini G, Luu TB, Hargrove AE. Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA. RNA. 2023;29(4):473–488. doi: 10.1261/rna.079497.122
   PubMed Web of Science ®Google Scholar
• Zogg H, Singh R, Ro S. Current advances in RNA therapeutics for human diseases. Int J Mol Sci. 2022;23(5):2736. doi: 10.3390/ijms23052736
   PubMed Web of Science ®Google Scholar
• Rabie AM. Two antioxidant 2,5-disubstituted-1,3,4-oxadiazoles (CoVitris2020 and ChloViD2020): successful repurposing against COVID-19 as the first potent multitarget anti-SARS-CoV-2 drugs. New J Chem. 2021;45(2):761–771. doi: 10.1039/D0NJ03708G
   Web of Science ®Google Scholar
• Rabie AM. Teriflunomide: a possible effective drug for the comprehensive treatment of COVID-19. Current Res Pharmacol Drug Discov. 2021;2:100055. doi: 10.1016/j.crphar.2021.100055
   PubMedGoogle Scholar
• Rabie AM. Potent inhibitory activities of the adenosine analogue cordycepin on SARS-CoV-2 replication. ACS Omega. 2022;7(3):2960–2969. doi: 10.1021/acsomega.1c05998
   PubMed Web of Science ®Google Scholar
• Rabie AM, Abdalla M. Evaluation of a series of nucleoside analogs as effective anticoronaviral-2 drugs against the Omicron-B.1.1.529/BA.2 subvariant: a repurposing research study. Med Chem Res. 2023;32(2):326–341. doi: 10.1007/s00044-022-02970-3
   PubMed Web of Science ®Google Scholar
• Koehn JT, Felder S, Weeks KM. Innovations in targeting RNA by fragment-based ligand discovery. Curr Opin Struct Biol. 2023;79:102550. doi: 10.1016/j.sbi.2023.102550
   PubMed Web of Science ®Google Scholar
• Sheridan C. First small-molecule drug targeting RNA gains momentum. Nat Biotechnol. 2021;39(1):6–8. doi: 10.1038/s41587-020-00788-1
   PubMed Web of Science ®Google Scholar
• Jiang W, Chen X, Liao M, et al. Identification of links between small molecules and miRnas in human cancers based on transcriptional responses. Sci Rep. 2012;2(1):282. doi: 10.1038/srep00282
   PubMedGoogle Scholar
• Wang J, Meng F, Dai E, et al. Identification of associations between small molecule drugs and miRnas based on functional similarity. Oncotarget. 2016;7(25):38658–38669. doi: 10.18632/oncotarget.9577.
   PubMedGoogle Scholar
• Qu J, Chen X, Sun YZ, et al. Inferring potential small molecule-miRNA association based on triple layer heterogeneous network. J Cheminform. 2018;10(1):30. doi: 10.1186/s13321-018-0284-9
   PubMedGoogle Scholar
• Lv Y, Wang S, Meng F, et al. Identifying novel associations between small molecules and miRnas based on integrated molecular networks. Bioinformatics. 2015;31(22):3638–3644. doi: 10.1093/bioinformatics/btv417
   PubMed Web of Science ®Google Scholar
• Li J, Lei K, Wu Z, et al. Network-based identification of microRnas as potential pharmacogenomic biomarkers for anticancer drugs. Oncotarget. 2016;7(29):45584–45596. doi: 10.18632/oncotarget.10052
   PubMedGoogle Scholar
• Qu J, Chen X, Sun YZ, et al. In Silico prediction of small molecule-miRNA associations based on the HeteSim algorithm. Mol Ther Nucleic Acids. 2019;14:274–286. doi: 10.1016/j.omtn.2018.12.002
   PubMedGoogle Scholar
• Meng F, Dai E, Yu X, et al. Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer’s disease. J R Soc Interface. 2014;11(92):20131057. doi: 10.1098/rsif.2013.1057
   PubMed Web of Science ®Google Scholar
• Guan NN, Sun YZ, Ming Z, et al. Prediction of potential small molecule-associated MicroRNAs using graphlet interaction. Front Pharmacol. 2018;9:1152. doi: 10.3389/fphar.2018.01152
   PubMed Web of Science ®Google Scholar
• Lu M, Yin J, Zhu Q, et al. Artificial intelligence in pharmaceutical sciences. Eng. 2023. doi:10.1016/j.eng.2023.01.014
   Google Scholar