{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:20:12Z","timestamp":1784031612581,"version":"3.55.0"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3600902"],"award-info":[{"award-number":["2022YFC3600902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Province Soft Science Key Project","award":["2022C25013"],"award-info":[{"award-number":["2022C25013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Aptamers are single-stranded nucleic acid ligands, featuring high affinity and specificity to target molecules. Traditionally they are identified from large DNA\/RNA libraries using $in vitro$ methods, like Systematic Evolution of Ligands by Exponential Enrichment (SELEX). However, these libraries capture only a small fraction of theoretical sequence space, and various aptamer candidates are constrained by actual sequencing capabilities from the experiment. Addressing this, we proposed AptaDiff, the first in silico aptamer design and optimization method based on the diffusion model. Our Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data, leveraging motif-dependent latent embeddings from variational autoencoder, and can optimize aptamers by affinity-guided aptamer generation according to Bayesian optimization. Comparative evaluations revealed AptaDiff\u2019s superiority over existing aptamer generation methods in terms of quality and fidelity across four high-throughput screening data targeting distinct proteins. Moreover, surface plasmon resonance experiments were conducted to validate the binding affinity of aptamers generated through Bayesian optimization for two target proteins. The results unveiled a significant boost of $87.9\\%$ and $60.2\\%$ in RU values, along with a 3.6-fold and 2.4-fold decrease in KD values for the respective target proteins. Notably, the optimized aptamers demonstrated superior binding affinity compared to top experimental candidates selected through SELEX, underscoring the promising outcomes of our AptaDiff in accelerating the discovery of superior aptamers.<\/jats:p>","DOI":"10.1093\/bib\/bbae517","type":"journal-article","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T15:15:11Z","timestamp":1728141311000},"source":"Crossref","is-referenced-by-count":42,"title":["AptaDiff: de novo design and optimization of aptamers based on diffusion models"],"prefix":"10.1093","volume":"25","author":[{"given":"Zhen","family":"Wang","sequence":"first","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"College of Electrical and Information Engineering, Hunan University , Changsha, 410082 Hunan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"School of Molecular Medicine, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences , Hangzhou, 310024 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry , Center for Natural Products, Drug Discovery and Development, , Gainesville, FL 32610 ,","place":["United States"]},{"name":"University of Florida , Center for Natural Products, Drug Discovery and Development, , Gainesville, FL 32610 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhen","family":"Feng","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"College of Information Engineering, Zhejiang University of Technology , Hangzhou, 310014 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaokang","family":"Lv","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"Department of Chemical Biology, Zhejiang University of Technology , Huzhou, 313200 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Diao","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"Department of Chemical Biology, Zhejiang University of Technology , Huzhou, 313200 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaofeng","family":"Luo","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengju","family":"Yan","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"ElasticMind Inc , Hangzhou, 310018 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"He","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"College of Electrical and Information Engineering, Hunan University , Changsha, 410082 Hunan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Li","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang ,","place":["China"]},{"name":"ElasticMind Inc , Hangzhou, 310018 Zhejiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"2024102109031042100_ref1","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1517\/13543770903313746","article-title":"Aptamers: from bench side research towards patented molecules with therapeutic applications","volume":"19","author":"Majumder","year":"2009","journal-title":"Expert Opin Ther Pat"},{"key":"2024102109031042100_ref2","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1038\/nrd3141","article-title":"Aptamers as therapeutics","volume":"9","author":"Keefe","year":"2010","journal-title":"Nat Rev Drug Discov"},{"key":"2024102109031042100_ref3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2012\/748913","article-title":"Challenges and opportunities for small molecule aptamer development","volume":"2012","author":"McKeague","year":"2012","journal-title":"J Nucleic Acids"},{"key":"2024102109031042100_ref4","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":"2024102109031042100_ref5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nature00963","article-title":"rna aptamers as reversible antagonists of coagulation factor IXa","volume":"419","author":"Rusconi","year":"2002","journal-title":"Nature"},{"key":"2024102109031042100_ref6","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1038\/nbt1023","article-title":"Antidote-mediated control of an anticoagulant aptamer in vivo","volume":"22","author":"Rusconi","year":"2004","journal-title":"Nat Biotechnol"},{"key":"2024102109031042100_ref7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S1389-0352(99)00004-5","article-title":"Aptamers as therapeutic and diagnostic agents","volume":"74","author":"Brody","year":"2000","journal-title":"Rev Mol Biotechnol"},{"key":"2024102109031042100_ref8","doi-asserted-by":"publisher","first-page":"2221","DOI":"10.1002\/anie.202003563","article-title":"Nucleic acid aptamers for molecular diagnostics and therapeutics: advances and perspectives","volume":"60","author":"Li","year":"2021","journal-title":"Angew Chem Int Ed"},{"key":"2024102109031042100_ref9","doi-asserted-by":"publisher","first-page":"5850","DOI":"10.2174\/0929867329666220224155037","article-title":"Recent advances in the selection of cancer-specific aptamers for the development of biosensors","volume":"29","author":"Sousa","year":"2022","journal-title":"Curr Med Chem"},{"key":"2024102109031042100_ref10","doi-asserted-by":"publisher","DOI":"10.1038\/npre.2010.4538.1","article-title":"Aptamer-based multiplexed proteomic technology for biomarker discovery","author":"Gold","year":"2010","journal-title":"Nat Preced"},{"key":"2024102109031042100_ref11","doi-asserted-by":"publisher","first-page":"8642","DOI":"10.1021\/ac101801j","article-title":"Aptamers recognizing glycosylated hemagglutinin expressed on the surface of vaccinia virus-infected cells","volume":"82","author":"Parekh","year":"2010","journal-title":"Anal Chem"},{"key":"2024102109031042100_ref12","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1038\/leu.2008.335","article-title":"Molecular recognition of acute myeloid leukemia using aptamers","volume":"23","author":"Kwame Sefah","year":"2009","journal-title":"Leukemia"},{"key":"2024102109031042100_ref13","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1021\/cn100114k","article-title":"In vitro selection of DNA aptamers to glioblastoma multiforme","volume":"2","author":"Bayrac","year":"2011","journal-title":"ACS Chem Nerosci"},{"key":"2024102109031042100_ref14","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/S0956-5663(99)00028-7","article-title":"In vitro selection of DNA aptamers to anthrax spores with electrochemiluminescence detection","volume":"14","author":"Bruno","year":"1999","journal-title":"Biosens Bioelectron"},{"key":"2024102109031042100_ref15","doi-asserted-by":"publisher","first-page":"4066","DOI":"10.1021\/ac049858n","article-title":"Aptamer-based sensor arrays for the detection and quantitation of proteins","volume":"76","author":"Kirby","year":"2004","journal-title":"Anal Chem"},{"key":"2024102109031042100_ref16","doi-asserted-by":"crossref","first-page":"11838","DOI":"10.1073\/pnas.0602615103","article-title":"Aptamers evolved from live cells as effective molecular probes for cancer study","volume":"103","author":"Shangguan","year":"2006","journal-title":"Proc Natl Acad Sci"},{"key":"2024102109031042100_ref17","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1373\/clinchem.2008.113514","article-title":"Generating aptamers for recognition of virus-infected cells","volume":"55","author":"Tang","year":"2009","journal-title":"Clin Chem"},{"key":"2024102109031042100_ref18","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1097\/00006982-200204000-00002","article-title":"Preclinical and phase 1a clinical evaluation of an anti-vegf pegylated aptamer (eye001) for the treatment of exudative age-related macular degeneration","volume":"22","author":"Eyetech Study Group","year":"2002","journal-title":"Retina"},{"key":"2024102109031042100_ref19","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1016\/S0161-6420(03)00085-X","article-title":"Anti-vascular endothelial growth factor therapy for subfoveal choroidal neovascularization secondary to age-related macular degeneration: Phase ii study results","volume":"110","author":"Eyetech Study Group","year":"2003","journal-title":"Ophthalmology"},{"key":"2024102109031042100_ref20","doi-asserted-by":"publisher","DOI":"10.1038\/mtna.2014.32","article-title":"Oligonucleotide aptamers: new tools for targeted cancer therapy","volume":"3","author":"Sun","year":"2014","journal-title":"Mol Ther-Nucleic Acids"},{"key":"2024102109031042100_ref21","doi-asserted-by":"publisher","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":"2024102109031042100_ref22","doi-asserted-by":"publisher","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":"2024102109031042100_ref23","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1074-5521(97)90315-X","article-title":"Accessing rare activities from random RNA sequences: the importance of the length of molecules in the starting pool","volume":"4","author":"Sabeti","year":"1997","journal-title":"Chem Biol"},{"key":"2024102109031042100_ref24","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1038\/s41587-023-01973-8","article-title":"High-affinity one-step aptamer selection using a non-fouling porous hydrogel","volume":"42","author":"Singh","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2024102109031042100_ref25","doi-asserted-by":"publisher","first-page":"6074","DOI":"10.1038\/s41598-021-85629-0","article-title":"Aptanet as a deep learning approach for aptamer\u2013protein interaction prediction","volume":"11","author":"Emami","year":"2021","journal-title":"Sci Rep"},{"key":"2024102109031042100_ref26","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1038\/s41587-022-01307-0","article-title":"Prediction of protein\u2013ligand binding affinity from sequencing data with interpretable machine learning","volume":"40","author":"Tomas Rube","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024102109031042100_ref27","doi-asserted-by":"publisher","first-page":"2366","DOI":"10.1038\/s41467-021-22555-9","article-title":"Machine learning guided aptamer refinement and discovery","volume":"12","author":"Bashir","year":"2021","journal-title":"Nat Commun"},{"key":"2024102109031042100_ref28","doi-asserted-by":"publisher","first-page":"e139","DOI":"10.1093\/nar\/gkq282","article-title":"Computational generation and screening of RNA motifs in large nucleotide sequence pools","volume":"38","author":"Kim","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"2024102109031042100_ref29","doi-asserted-by":"publisher","first-page":"5699","DOI":"10.1093\/nar\/gkv308","article-title":"Large scale analysis of the mutational landscape in HT-SELEX improves aptamer discovery","volume":"43","author":"Hoinka","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2024102109031042100_ref30","doi-asserted-by":"publisher","first-page":"5939","DOI":"10.1021\/acs.jctc.5b00707","article-title":"Searching the sequence space for potent aptamers using selex in silico","volume":"11","author":"Zhou","year":"2015","journal-title":"J Chem Theory Comput"},{"key":"2024102109031042100_ref31","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat Biotechnol"},{"key":"2024102109031042100_ref32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1145\/3534678.3539343","article-title":"Motif prediction with graph neural networks","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Besta","year":"2022"},{"key":"2024102109031042100_ref33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-018-4889-1","article-title":"Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks","volume":"19","author":"Pan","year":"2018","journal-title":"BMC Genomics"},{"key":"2024102109031042100_ref34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6299-4","article-title":"A generative model for constructing nucleic acid sequences binding to a protein","volume":"20","author":"Im","year":"2019","journal-title":"BMC Genomics"},{"key":"2024102109031042100_ref35","article-title":"Generative adversarial nets","volume":"2","author":"Goodfellow","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref36","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"2024102109031042100_ref37","article-title":"Generating and designing DNA with deep generative models","author":"Killoran","year":"2017"},{"key":"2024102109031042100_ref38","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1101\/2023.07.11.548246","article-title":"RNAGEN: a generative adversarial network-based model to generate synthetic RNA sequences to target proteins","author":"Ozden","year":"2023","journal-title":"bioRxiv"},{"key":"2024102109031042100_ref39","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1038\/s43588-022-00249-6","article-title":"Generative aptamer discovery using RaptGen","volume":"2","author":"Iwano","year":"2022","journal-title":"Nat Comput Sci"},{"key":"2024102109031042100_ref40","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"International Conference on Machine Learning","author":"Sohl-Dickstein","year":"2015"},{"key":"2024102109031042100_ref41","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref42","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref43","article-title":"GeoDiff: a geometric diffusion model for molecular conformation generation","author":"Xu","year":"2022"},{"key":"2024102109031042100_ref44","volume-title":"Pocket-Specific 3D Molecule Generation by Fragment-Based Autoregressive Diffusion Models","author":"Peng","year":"2023"},{"key":"2024102109031042100_ref45","article-title":"Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem","author":"Trippe","year":"2022"},{"key":"2024102109031042100_ref46","first-page":"9754","article-title":"Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures","volume":"35","author":"Luo","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref47","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1101\/2023.05.08.539766","article-title":"Joint generation of protein sequence and structure with rosettafold sequence space diffusion","author":"Lisanza","year":"2023","journal-title":"bioRxiv"},{"key":"2024102109031042100_ref48","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1101\/2023.05.24.542194","article-title":"An all-atom protein generative model","author":"Chu","year":"2023","journal-title":"bioRxiv"},{"key":"2024102109031042100_ref49","first-page":"12454","article-title":"Argmax flows and multinomial diffusion: learning categorical distributions","volume":"34","author":"Hoogeboom","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref50","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/30.1.27","article-title":"DNA Data Bank of Japan (DDBJ) for genome scale research in life science","volume":"30","author":"Tateno","year":"2002","journal-title":"Nucleic Acids Res"},{"key":"2024102109031042100_ref51","doi-asserted-by":"crossref","first-page":"D22","DOI":"10.1093\/nar\/gkq1041","article-title":"DDBJ progress report","volume":"39","author":"Kaminuma","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"2024102109031042100_ref52","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref53","article-title":"DiffuseVAE: efficient, controllable and high-fidelity generation from low-dimensional latents","author":"Pandey","year":"2022"},{"key":"2024102109031042100_ref54","first-page":"8024","article-title":"PyTorch: an imperative style, high-performance deep learning library","volume-title":"Advances in Neural Information Processing Systems 32","author":"Paszke","year":"2019"},{"key":"2024102109031042100_ref55","first-page":"707","article-title":"Binary codes capable of correcting deletions, insertions, and reversals","volume-title":"Soviet Physics Doklady","author":"Levenshtein","year":"1966"},{"key":"2024102109031042100_ref56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1748-7188-6-26","article-title":"Viennarna package 2.0","volume":"6","author":"Lorenz","year":"2011","journal-title":"Algorithms Mol Biol"},{"key":"2024102109031042100_ref57","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume":"30","author":"Heusel","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref58","doi-asserted-by":"crossref","article-title":"Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions","author":"Chen","DOI":"10.1101\/2022.08.06.503062"},{"key":"2024102109031042100_ref59","article-title":"DNABERT-2: efficient foundation model and benchmark for multi-species genome","author":"Zhou","year":"2023"},{"key":"2024102109031042100_ref60","volume-title":"Fitting a Mixture Model by Expectation Maximization to Discover Motifs in Bipolymers","author":"Bailey","year":"1994"},{"key":"2024102109031042100_ref61","doi-asserted-by":"publisher","first-page":"2701","DOI":"10.1039\/D1SC05976A","article-title":"Generating 3D molecules conditional on receptor binding sites with deep generative models","volume":"13","author":"Ragoza","year":"2022","journal-title":"Chem Sci"},{"key":"2024102109031042100_ref62","article-title":"Generating 3D molecules for target protein binding","author":"Liu","year":"2022"},{"key":"2024102109031042100_ref63","first-page":"6229","article-title":"A 3D generative model for structure-based drug design","volume":"34","author":"Luo","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102109031042100_ref64","first-page":"17644","article-title":"Pocket2Mol: efficient molecular sampling based on 3D protein pockets","volume-title":"International Conference on Machine Learning","author":"Peng","year":"2022"},{"key":"2024102109031042100_ref65","article-title":"GPyOpt: a Bayesian optimization framework in Python","author":"The GPyOpt authors","year":"2016"},{"key":"2024102109031042100_ref66","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1038\/s41467-021-21194-4","article-title":"Rna secondary structure prediction using deep learning with thermodynamic integration","volume":"12","author":"Sato","year":"2021","journal-title":"Nat Commun"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/6\/bbae517\/59923079\/bbae517.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/6\/bbae517\/59923079\/bbae517.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T05:03:49Z","timestamp":1729487029000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae517\/7828722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,23]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,9,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae517","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.11.25.568693","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,11]]},"published":{"date-parts":[[2024,9,23]]},"article-number":"bbae517"}}