{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T13:11:12Z","timestamp":1789132272376,"version":"build-2803163510"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","award":["351507"],"award-info":[{"award-number":["351507"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Therapies targeting RNA are rapidly expanding, with 24 FDA-approved RNA therapeutics and over 130 currently in clinical trials, highlighting RNA\u2019s growing role in drug discovery. In this context, transformer-based language models provide a scalable and cost-effective approach to accelerate RNA-targeted drug discovery by enabling the prediction of molecule\u2013RNA binding affinities directly from sequence information. This study introduces DLRNA-BERTa, a RoBERTa-based framework that integrates RNA-BERTa and ChemBERTa-v2 to model molecule\u2013RNA binding affinities. The framework includes six RNA class-specific models, aptamers, repeats, ribosomal RNAs, riboswitches, microRNAs, and viral RNAs, along with a general model for other RNA classes. DLRNA-BERTa outperformed existing approaches across different RNA classes on a randomly split validation dataset. On an independent and relatively diverse test set, the model achieved AUROC values of 0.57\u20130.59, demonstrating performance comparable to the other evaluated methods.<\/jats:p>\n                  <jats:p>Application of DLRNA-BERTa to a library of 3492 approved drugs identified 2859 compounds with predicted binding affinities (pKd\u2009\u2265\u20096) across 294 RNA targets, suggesting its potential utility for RNA-based drug repurposing and prioritization of candidate molecules for further investigation. To facilitate broader use and reproducibility, we provide a publicly accessible web application and API are available at https:\/\/huggingface.co\/spaces\/IlPakoZ\/DLRNA-BERTa, enabling users to predict binding affinities between custom compounds and RNA sequences.<\/jats:p>","DOI":"10.1093\/bib\/bbag488","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T11:51:18Z","timestamp":1787140278000},"source":"Crossref","is-referenced-by-count":0,"title":["DLRNA-BERTa: a transformer approach for predicting molecule-RNA binding affinities"],"prefix":"10.1093","volume":"27","author":[{"given":"Pasquale","family":"Lobascio","sequence":"first","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki , Tukholmankatu 8, FI-00290 Helsinki ,","place":["Finland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid","family":"Saeed","sequence":"additional","affiliation":[{"name":"Institute for Molecular 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