{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T04:32:23Z","timestamp":1792384343186,"version":"4.4.0"},"reference-count":105,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program from the Ministry of Science and Technology of China","award":["2018YFA0800800"],"award-info":[{"award-number":["2018YFA0800800"]}]},{"name":"Hong Kong General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region, China","award":["12102120"],"award-info":[{"award-number":["12102120"]}]},{"name":"Theme-based Research Scheme from the Research Grants Council of the Hong Kong Special Administrative Region, China","award":["T12-201\/20-R"],"award-info":[{"award-number":["T12-201\/20-R"]}]},{"name":"Basic and Applied Basic Research Fund from Department of Science and Technology of Guangdong Province","award":["2019B1515120089"],"award-info":[{"award-number":["2019B1515120089"]}]},{"name":"Inter-institutional Collaborative Research Scheme from Hong Kong Baptist University","award":["RC-ICRS\/19-20\/01"],"award-info":[{"award-number":["RC-ICRS\/19-20\/01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines\/methods, including structure-based and machine\/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer\u2013target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-\/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-\/deep-learning techniques for prediction of aptamer\u2013target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine\/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.<\/jats:p>","DOI":"10.3390\/ijms22073605","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:13:10Z","timestamp":1617149590000},"page":"3605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":143,"title":["Artificial Intelligence in Aptamer\u2013Target Binding Prediction"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7690-8651","authenticated-orcid":false,"given":"Zihao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Hu","sequence":"additional","affiliation":[{"name":"Law Sau Fai Institute for Advancing Translational Medicine in Bone &amp; Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bao-Ting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiping","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China"},{"name":"Guangdong-Hong Kong Macao Greater Bay Area International Research Platform for Aptamer-Based Translational Medicine and Drug Discovery, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaofeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Centre for Regenerative Medicine and Health, Hong Kong Institute of Science &amp; Innovation, Chinese Academy of Sciences, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Law Sau Fai Institute for Advancing Translational Medicine in Bone &amp; Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China"},{"name":"Guangdong-Hong Kong Macao Greater Bay Area International Research Platform for Aptamer-Based Translational Medicine and Drug Discovery, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7807-7695","authenticated-orcid":false,"given":"Ge","family":"Zhang","sequence":"additional","affiliation":[{"name":"Law Sau Fai Institute for Advancing Translational Medicine in Bone &amp; Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China"},{"name":"Guangdong-Hong Kong Macao Greater Bay Area International Research Platform for Aptamer-Based Translational Medicine and Drug Discovery, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1126\/science.2200121","article-title":"Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase","volume":"249","author":"Tuerk","year":"1990","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1038\/346818a0","article-title":"In vitro selection of RNA molecules that bind specific ligands","volume":"346","author":"Ellington","year":"1990","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/nrd.2016.199","article-title":"Aptamers as targeted therapeutics: Current potential and challenges","volume":"16","author":"Zhou","year":"2017","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.talanta.2018.10.028","article-title":"In vitro selection of DNA aptamers recognizing drug-resistant ovarian cancer by cell-SELEX","volume":"194","author":"He","year":"2019","journal-title":"Talanta"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1590\/S1516-89132007000600008","article-title":"Aptamer-based Therapeutics and their Potential in Radiopharmaceutical Design","volume":"50","author":"Ferreira","year":"2007","journal-title":"Braz. Arch. Biol. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/s00216-007-1769-y","article-title":"Aptamers and their applications","volume":"390","author":"Mascini","year":"2008","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110902","DOI":"10.1016\/j.biopha.2020.110902","article-title":"Aptamers used for biosensors and targeted therapy","volume":"132","author":"Ning","year":"2020","journal-title":"Biomed. Pharmacother."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, Y., Liang, C., Lv, Q., Li, D., Xu, X., Liu, B., Lu, A., and Zhang, G. (2016). Molecular Selection, Modification and Development of Therapeutic Oligonucleotide Aptamers. Int. J. Mol. Sci., 17.","DOI":"10.3390\/ijms17030358"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"423","DOI":"10.4062\/biomolther.2013.085","article-title":"Nucleic Acid aptamers: New methods for selection, stabilization, and application in biomedical science","volume":"21","author":"Kong","year":"2013","journal-title":"Biomol. Ther."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kinghorn, A.B., Fraser, L.A., Lang, S., Shiu, S.C.C., and Tanner, J.A. (2017). Aptamer Bioinformatics. Int. J. Mol. Sci., 18.","DOI":"10.3390\/ijms18122516"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Buglak, A.A., Samokhvalov, A.V., Zherdev, A.V., and Dzantiev, B.B. (2020). Methods and Applications of In Silico Aptamer Design and Modeling. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21228420"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e87","DOI":"10.1093\/nar\/gkp408","article-title":"In silico selection of RNA aptamers","volume":"37","author":"Chushak","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1093\/nar\/gkg599","article-title":"Vienna RNA secondary structure server","volume":"31","author":"Hofacker","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21285","DOI":"10.1038\/srep21285","article-title":"In silico selection of an aptamer to estrogen receptor alpha using computational docking employing estrogen response elements as aptamer-alike molecules","volume":"6","author":"Ahirwar","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"782","DOI":"10.3389\/fchem.2019.00782","article-title":"Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities","volume":"7","author":"Thafar","year":"2019","journal-title":"Front. Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1002\/wcms.1225","article-title":"Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening","volume":"5","author":"Ain","year":"2015","journal-title":"Wiley Interdiscip. Rev. Comput. Mol. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1903451","DOI":"10.1002\/advs.201903451","article-title":"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","volume":"7","author":"Zhuo","year":"2020","journal-title":"Adv. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sullivan, R., Adams, M.C., Naik, R.R., and Milam, V.T. (2019). Analyzing Secondary Structure Patterns in DNA Aptamers Identified via CompELS. Molecules, 24.","DOI":"10.3390\/molecules24081572"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"047006","DOI":"10.1117\/1.3465594","article-title":"Direct detection of aptamer-thrombin binding via surface-enhanced Raman spectroscopy","volume":"15","author":"Pagba","year":"2010","journal-title":"J. Biomed. Opt."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1038\/s41598-017-01348-5","article-title":"Three-dimensional modeling of single stranded DNA hairpins for aptamer-based biosensors","volume":"7","author":"Jeddi","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_21","first-page":"4108","article-title":"Applicability of a computational design approach for synthetic riboswitches","volume":"45","author":"Domin","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3406","DOI":"10.1093\/nar\/gkg595","article-title":"Mfold web server for nucleic acid folding and hybridization prediction","volume":"31","author":"Zuker","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jbiotec.2016.05.021","article-title":"Computational approach to analyze isolated ssDNA aptamers against angiotensin II","volume":"230","author":"Heiat","year":"2016","journal-title":"J. Biotechnol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"W471","DOI":"10.1093\/nar\/gkt290","article-title":"RNAstructure: Web servers for RNA secondary structure prediction and analysis","volume":"41","author":"Bellaousov","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1089\/nat.2011.0313","article-title":"Rational truncation of an RNA aptamer to prostate-specific membrane antigen using computational structural modeling","volume":"21","author":"Rockey","year":"2011","journal-title":"Nucleic Acid Ther."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-1-4939-7231-9_1","article-title":"Predicting RNA Structure with Vfold","volume":"1654","author":"Zhao","year":"2017","journal-title":"Methods Mol. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1093\/nar\/gkz1224","article-title":"Binding interface and impact on protease cleavage for an RNA aptamer to HIV-1 reverse transcriptase","volume":"48","author":"Nguyen","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"W277","DOI":"10.1093\/nar\/gkp367","article-title":"CENTROIDFOLD: A web server for RNA secondary structure prediction","volume":"37","author":"Sato","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"658712","DOI":"10.1155\/2015\/658712","article-title":"Computational selection of RNA aptamer against angiopoietin-2 and experimental evaluation","volume":"2015","author":"Hu","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1093\/nar\/9.1.133","article-title":"Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information","volume":"9","author":"Zuker","year":"1981","journal-title":"Nucleic Acids Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1261\/rna.1643609","article-title":"Improved RNA secondary structure prediction by maximizing expected pair accuracy","volume":"15","author":"Lu","year":"2009","journal-title":"RNA"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7280","DOI":"10.1093\/nar\/gkg938","article-title":"A statistical sampling algorithm for RNA secondary structure prediction","volume":"31","author":"Ding","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9819","DOI":"10.1021\/bi052618x","article-title":"Interpreting oligonucleotide microarray data to determine RNA secondary structure: Application to the 3\u2032 end of Bombyx mori R2 RNA","volume":"45","author":"Duan","year":"2006","journal-title":"Biochemistry"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1261\/rna.2125310","article-title":"ProbKnot: Fast prediction of RNA secondary structure including pseudoknots","volume":"16","author":"Bellaousov","year":"2010","journal-title":"RNA"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1002\/cphc.201700363","article-title":"The Bound Structures of 17beta-Estradiol-Binding Aptamers","volume":"18","author":"Hilder","year":"2017","journal-title":"Eur. J. Chem. Phys. Phys. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhao, P., and Chen, S.J. (2014). Vfold: A web server for RNA structure and folding thermodynamics prediction. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0107504"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"W30","DOI":"10.1093\/nar\/gky314","article-title":"RNApdbee 2.0: Multifunctional tool for RNA structure annotation","volume":"46","author":"Zok","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ymeth.2016.03.010","article-title":"RNAComposer and RNA 3D structure prediction for nanotechnology","volume":"103","author":"Biesiada","year":"2016","journal-title":"Methods"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, J., Huang, Y., and Xiao, Y. (2019). 3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20174116"},{"key":"ref_40","first-page":"5","article-title":"In silico predictions and optimization of aptamers against Streptococcus agalactiae surface protein using computational docking","volume":"16","author":"Soon","year":"2019","journal-title":"Mater. Today Proc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.ymeth.2016.03.004","article-title":"Structural computational modeling of RNA aptamers","volume":"103","author":"Xu","year":"2016","journal-title":"Methods"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e63","DOI":"10.1093\/nar\/gkv1479","article-title":"SimRNA: A coarse-grained method for RNA folding simulations and 3D structure prediction","volume":"44","author":"Boniecki","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.compbiolchem.2018.09.014","article-title":"A validation strategy for in silico generated aptamers","volume":"77","author":"Cataldo","year":"2018","journal-title":"Comput. Biol. Chem."},{"key":"ref_44","first-page":"7","article-title":"In silico post-SELEX screening and experimental characterizations for acquisition of high affinity DNA aptamers against carcinoembryonic antigen","volume":"9","author":"Wang","year":"2019","journal-title":"RSC Adv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6912914","DOI":"10.1155\/2019\/6912914","article-title":"In Silico Screening of Aptamers Configuration against Hepatitis B Surface Antigen","volume":"2019","author":"Sabri","year":"2019","journal-title":"Adv. Bioinform."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s12539-019-00327-w","article-title":"An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking","volume":"11","author":"Li","year":"2019","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1093\/bioinformatics\/btu097","article-title":"ZDOCK server: Interactive docking prediction of protein-protein complexes and symmetric multimers","volume":"30","author":"Pierce","year":"2014","journal-title":"Bioinformatics"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pierce, B.G., Hourai, Y., and Weng, Z. (2011). Accelerating protein docking in ZDOCK using an advanced 3D convolution library. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0024657"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3096","DOI":"10.1002\/prot.22797","article-title":"MDockPP: A hierarchical approach for protein-protein docking and its application to CAPRI rounds 15-19","volume":"78","author":"Huang","year":"2010","journal-title":"Proteins"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1186\/1479-7364-5-5-497","article-title":"Survey of public domain software for docking simulations and virtual screening","volume":"5","author":"Biesiada","year":"2011","journal-title":"Hum. Genom."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1261\/rna.1563609","article-title":"DOCK 6: Combining techniques to model RNA-small molecule complexes","volume":"15","author":"Lang","year":"2009","journal-title":"RNA"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jsb.2015.07.003","article-title":"Computer-aided design of aptamers for cytochrome p450","volume":"191","author":"Shcherbinin","year":"2015","journal-title":"J. Struct. Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J. Comput. Chem."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Quiroga, R., and Villarreal, M.A. (2016). Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0155183"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Vieira, T.E., and Sousa, S.F. (2019). Comparing AutoDock and Vina in Ligand\/Decoy Discrimination for Virtual Screening. Appl. Sci., 9.","DOI":"10.3390\/app9214538"},{"key":"ref_56","first-page":"7","article-title":"GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers","volume":"1","author":"Abraham","year":"2015","journal-title":"SoftwareX"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1002\/jcc.20290","article-title":"The Amber biomolecular simulation programs","volume":"26","author":"Case","year":"2005","journal-title":"J. Comput. Chem."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1093\/bioinformatics\/btt055","article-title":"GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit","volume":"29","author":"Pronk","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1517\/17460441.2015.1032936","article-title":"The MM\/PBSA and MM\/GBSA methods to estimate ligand-binding affinities","volume":"10","author":"Genheden","year":"2015","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.2174\/092986711796504727","article-title":"Novel modular DNA aptamer for human thrombin with high anticoagulant activity","volume":"18","author":"Zavyalova","year":"2011","journal-title":"Curr. Med. Chem."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.bbagen.2016.11.027","article-title":"G-quadruplex-based aptamers against protein targets in therapy and diagnostics","volume":"1861","author":"Platella","year":"2017","journal-title":"Biochim. Biophys. Acta Gen. Subj."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1039\/B608494J","article-title":"Synthesis and G-quadruplex binding studies of new 4-N-methylpyridinium porphyrins","volume":"4","author":"Goncalves","year":"2006","journal-title":"Org. Biomol. Chem."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"107649","DOI":"10.1016\/j.pharmthera.2020.107649","article-title":"G-quadruplex-based aptamers targeting human thrombin: Discovery, chemical modifications and antithrombotic effects","volume":"217","author":"Riccardi","year":"2021","journal-title":"Pharmacol. Ther."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Roxo, C., Kotkowiak, W., and Pasternak, A. (2019). G-Quadruplex-Forming Aptamers-Characteristics, Applications, and Perspectives. Molecules, 24.","DOI":"10.3390\/molecules24203781"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1006\/jmbi.1997.1275","article-title":"Oligonucleotide inhibitors of human thrombin that bind distinct epitopes","volume":"272","author":"Tasset","year":"1997","journal-title":"J. Mol. Biol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.ymeth.2007.05.007","article-title":"NMR methods for studying quadruplex nucleic acids","volume":"43","year":"2007","journal-title":"Methods"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.ymeth.2007.08.005","article-title":"Crystallographic studies of quadruplex nucleic acids","volume":"43","author":"Campbell","year":"2007","journal-title":"Methods"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/nar\/gkaa033","article-title":"A guide to computational methods for G-quadruplex prediction","volume":"48","author":"Lombardi","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.ijbiomac.2019.04.141","article-title":"The role of G-quadruplex structures of LIGS-generated aptamers R1.2 and R1.3 in IgM specific recognition","volume":"133","author":"Moccia","year":"2019","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.2174\/138161212799958477","article-title":"G-quadruplex DNA aptamers and their ligands: Structure, function and application","volume":"18","author":"Tucker","year":"2012","journal-title":"Curr. Pharm. Des."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Troisi, R., Balasco, N., Vitagliano, L., and Sica, F. (2020). Molecular dynamics simulations of human \u03b1-thrombin in different structural contexts: Evidence for an aptamer-guided cooperation between the two exosites. J. Biomol. Struct. Dyn., 1\u201311.","DOI":"10.1080\/07391102.2020.1746693"},{"key":"ref_72","unstructured":"Mitchell, T. (1997). Machine Learning, McGraw Hill."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_74","unstructured":"Wornow, M. (2020). Applying Deep Learning to Discover Highly Functionalized Nucleic Acid Polymers that Bind to Small Molecules. [Ph.D. Thesis, Harvard University]."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hoinka, J., Berezhnoy, A., Sauna, Z.E., Gilboa, E., and Przytycka, T.M. (2014, January 2\u20135). AptaCluster\u2014A Method to Cluster HT-SELEX Aptamer Pools and Lessons from its Application. Proceedings of the International Conference on Research in Computational Molecular Biology, Pittsburgh, PA, USA.","DOI":"10.1007\/978-3-319-05269-4_9"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"e230","DOI":"10.1038\/mtna.2015.4","article-title":"FASTAptamer: A Bioinformatic Toolkit for High-throughput Sequence Analysis of Combinatorial Selections","volume":"4","author":"Alam","year":"2015","journal-title":"Mol. Ther. Nucleic Acids"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.cels.2016.07.003","article-title":"AptaTRACE Elucidates RNA Sequence-Structure Motifs from Selection Trends in HT-SELEX Experiments","volume":"3","author":"Dao","year":"2016","journal-title":"Cell Syst."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1093\/bioinformatics\/btv545","article-title":"APTANI: A computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data","volume":"32","author":"Caroli","year":"2016","journal-title":"Bioinformatics"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"i215","DOI":"10.1093\/bioinformatics\/bts210","article-title":"Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers","volume":"28","author":"Hoinka","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1021\/acs.analchem.9b05203","article-title":"A Sequential Multidimensional Analysis Algorithm for Aptamer Identification based on Structure Analysis and Machine Learning","volume":"92","author":"Song","year":"2020","journal-title":"Anal. Chem."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e82","DOI":"10.1093\/nar\/gkaa484","article-title":"RaptRanker: In silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information","volume":"48","author":"Ishida","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_82","unstructured":"Russell, S.J., and Norvig, P. (2020). Artificial Intelligence: A Modern Approach, Prentice Hall. [4th ed.]."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Li, B.Q., Zhang, Y.C., Huang, G.H., Cui, W.R., Zhang, N., and Cai, Y.D. (2014). Prediction of aptamer-target interacting pairs with pseudo-amino acid composition. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0086729"},{"key":"ref_84","first-page":"bas006","article-title":"Aptamer Base: A collaborative knowledge base to describe aptamers and SELEX experiments","volume":"2012","author":"McKeague","year":"2012","journal-title":"Database"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"012058","DOI":"10.1088\/1742-6596\/1229\/1\/012058","article-title":"An Effective Text Classification Model Based on Ensemble Strategy","volume":"1229","author":"Zhu","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_86","first-page":"31","article-title":"A thorough review on the current advance of neural network structures","volume":"14","author":"Dupond","year":"2019","journal-title":"Annu. Rev. Control"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/72.80210","article-title":"Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification","volume":"1","author":"Specht","year":"1990","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.matcom.2020.04.031","article-title":"Application of the residue number system to reduce hardware costs of the convolutional neural network implementation","volume":"177","author":"Valueva","year":"2020","journal-title":"Math. Comput. Simul."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/1062936X.2018.1558416","article-title":"Prediction of the binding affinity of aptamers against the influenza virus","volume":"30","author":"Yu","year":"2019","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1261\/rna.2164906","article-title":"RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers","volume":"12","author":"Bindewald","year":"2006","journal-title":"RNA"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1038\/s41467-019-13395-9","article-title":"RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning","volume":"10","author":"Singh","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"5381","DOI":"10.1093\/nar\/gky285","article-title":"bpRNA: Large-scale automated annotation and analysis of RNA secondary structure","volume":"46","author":"Danaee","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1038\/s41592-020-0958-x","article-title":"Predicting 3D genome folding from DNA sequence with Akita","volume":"17","author":"Fudenberg","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/bib\/bbu010","article-title":"Toward more realistic drug-target interaction predictions","volume":"16","author":"Pahikkala","year":"2015","journal-title":"Brief. Bioinform."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13321-017-0209-z","article-title":"SimBoost: A read-across approach for predicting drug-target binding affinities using gradient boosting machines","volume":"9","author":"He","year":"2017","journal-title":"J. Cheminform."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","article-title":"Gaussian interaction profile kernels for predicting drug-target interaction","volume":"27","author":"Nabuurs","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TCBB.2012.36","article-title":"A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction","volume":"9","author":"Ashtawy","year":"2012","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3666","DOI":"10.1093\/bioinformatics\/bty374","article-title":"Development and evaluation of a deep learning model for protein-ligand binding affinity prediction","volume":"34","author":"Zielenkiewicz","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Kwon, Y., Shin, W.H., Ko, J., and Lee, J. (2020). AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks. Int. J. Mol. Sci., 21.","DOI":"10.26434\/chemrxiv.12015045"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Ashtawy, H.M., and Mahapatra, N.R. (2015). BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. BMC Bioinform., 16.","DOI":"10.1186\/1471-2105-16-S4-S8"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1093\/bioinformatics\/btz111","article-title":"DeepAffinity: Interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks","volume":"35","author":"Karimi","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_102","unstructured":"Kalchbrenne, N., and Blunsom, P. (2013, January 18\u201321). Recurrent Continuous Translation Models. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, WA, USA."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: Deep drug-target binding affinity prediction","volume":"34","author":"Ozturk","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"\u00d6zt\u00fcrk, H., Ozkirimli, E., and \u00d6zg\u00fcr, A. (2019). WideDTA: Prediction of drug-target binding affinity. arXiv.","DOI":"10.1093\/bioinformatics\/bty593"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.3389\/fgene.2019.01243","article-title":"GANsDTA: Predicting Drug-Target Binding Affinity Using GANs","volume":"10","author":"Zhao","year":"2019","journal-title":"Front. Genet."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/22\/7\/3605\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:25:32Z","timestamp":1760361932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/22\/7\/3605"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,30]]},"references-count":105,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["ijms22073605"],"URL":"https:\/\/doi.org\/10.3390\/ijms22073605","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,30]]}}}