{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T01:12:29Z","timestamp":1788570749138,"version":"build-2803163510"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC114-2221-E-038\u2013015"],"award-info":[{"award-number":["NSTC114-2221-E-038\u2013015"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC115-2221-E-038\u2013012-MY3"],"award-info":[{"award-number":["NSTC115-2221-E-038\u2013012-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>RNA\u2013small molecule binding site prediction is crucial for targeted drug discovery. Sequence-based methods are efficient but often fail to capture structural dependencies between nucleotides, whereas structure-aware graph models can better represent spatial interactions but typically rely on complex structural annotations and multi-stage preprocessing pipelines. We therefore developed GRASSP, a streamlined hybrid deep learning framework that integrates pretrained RNA language model (LM) representations with adaptive graph refinement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>GRASSP leverages nucleotide embeddings and predicted secondary-structure features from a pretrained RNA LM to construct spatial RNA graphs, followed by a lightweight two-step graph attention refinement module with adaptive gating to capture local and contextual nucleotide dependencies. Across four benchmark datasets (TE18, HARIBOSS, TL12, and JL10), GRASSP generally outperformed state-of-the-art baselines, with improvements of up to 24.1% in AUC and 44.5% in MCC. Ablation analyses showed that pretrained RNA representations provided the dominant predictive contribution, while spatial graph refinement offered complementary but dataset-dependent benefits. These results demonstrate that GRASSP provides a competitive framework for integrating pretrained RNA representations with spatial structural context while reducing reliance on additional handcrafted structural annotations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>Code and datasets are publicly available at https:\/\/github.com\/langiocn\/GRASSP, with an archival snapshot available on Zenodo at https:\/\/doi.org\/10.5281\/zenodo.21888291.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag638","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:44:23Z","timestamp":1787312663000},"source":"Crossref","is-referenced-by-count":0,"title":["GRASSP: RNA language model\u2013enhanced graph attention with adaptive gating for RNA\u2013small molecule binding site prediction"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0654-350X","authenticated-orcid":false,"given":"Thi Lan","family":"Nguyen","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The University of Queensland , St Lucia, Brisbane, QLD 4072,","place":["Australia"]},{"name":"AIBioMed Lab, Taipei Medical University , Taipei 110,","place":["Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4896-7926","authenticated-orcid":false,"given":"Nguyen Quoc Khanh","family":"Le","sequence":"additional","affiliation":[{"name":"AIBioMed Lab, Taipei Medical University , Taipei 110,","place":["Taiwan"]},{"name":"In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University , Taipei 110,","place":["Taiwan"]},{"name":"Translational Imaging Research Center, Taipei Medical University Hospital , Taipei 110,","place":["Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,22]]},"reference":[{"key":"2026090420313290900_btag638-B1","doi-asserted-by":"crossref","first-page":"lqac012","DOI":"10.1093\/nargab\/lqac012","article-title":"Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning","volume":"4","author":"Akiyama","year":"2022","journal-title":"NAR Genom Bioinform"},{"key":"2026090420313290900_btag638-B2","doi-asserted-by":"crossref","first-page":"D464","DOI":"10.1093\/nar\/gky1004","article-title":"RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy","volume":"47","author":"Burley","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2026090420313290900_btag638-B3","doi-asserted-by":"crossref","first-page":"16002","DOI":"10.1021\/acs.jmedchem.4c01330","article-title":"RNA-binding small molecules in drug discovery and delivery: an overview from fundamentals","volume":"67","author":"Chen","year":"2024","journal-title":"J Med Chem"},{"key":"2026090420313290900_btag638-B4","doi-asserted-by":"crossref","first-page":"bbaf489","DOI":"10.1093\/bib\/bbaf489","article-title":"MVRBind: multi-view learning for RNA-small molecule binding site prediction","volume":"26","author":"Chen","year":"2025","journal-title":"Brief Bioinform"},{"key":"2026090420313290900_btag638-B5","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/nbt1273","article-title":"Structure-based maximal affinity model predicts small-molecule druggability","volume":"25","author":"Cheng","year":"2007","journal-title":"Nat Biotechnol"},{"key":"2026090420313290900_btag638-B6","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1038\/s41573-022-00521-4","article-title":"Targeting RNA structures with small molecules","volume":"21","author":"Childs-Disney","year":"2022","journal-title":"Nat Rev Drug Discov"},{"key":"2026090420313290900_btag638-B7","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1038\/nrg3074","article-title":"Non-coding RNAs in human disease","volume":"12","author":"Esteller","year":"2011","journal-title":"Nat Rev Genet"},{"key":"2026090420313290900_btag638-B8","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/15476286.2023.2231708","article-title":"Characterizing RNA-binding ligands on structures, chemical information, binding affinity and drug-likeness","volume":"20","author":"Fan","year":"2023","journal-title":"RNA Biol"},{"key":"2026090420313290900_btag638-B9","doi-asserted-by":"crossref","first-page":"6979","DOI":"10.1021\/acs.jcim.4c01264","article-title":"Predicting small molecule binding nucleotides in RNA structures using rna surface topography","volume":"64","author":"Gao","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2026090420313290900_btag638-B10","doi-asserted-by":"crossref","first-page":"bbaf713","DOI":"10.1093\/bib\/bbaf713","article-title":"Cobra: compound binding site prediction using RNA language model","volume":"27","author":"Jang","year":"2026","journal-title":"Brief Bioinform"},{"key":"2026090420313290900_btag638-B11","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1038\/s41580-025-00857-w","article-title":"Decoding the interactions and functions of non-coding RNA with artificial intelligence","volume":"26","author":"Jung","year":"2025","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2026090420313290900_btag638-B12","doi-asserted-by":"crossref","first-page":"bbad482","DOI":"10.1093\/bib\/bbad482","article-title":"RNet: a network strategy to predict RNA binding preferences","volume":"25","author":"Liu","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026090420313290900_btag638-B13","doi-asserted-by":"crossref","first-page":"4185","DOI":"10.1093\/bioinformatics\/btac483","article-title":"HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design","volume":"38","author":"Panei","year":"2022","journal-title":"Bioinformatics"},{"key":"2026090420313290900_btag638-B14","doi-asserted-by":"crossref","first-page":"5671","DOI":"10.1038\/s41467-025-60872-5","article-title":"RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks","volume":"16","author":"Peni\u0107","year":"2025","journal-title":"Nat Commun"},{"key":"2026090420313290900_btag638-B15","first-page":"8","article-title":"The pymol molecular graphics system","volume":"1","author":"Schrodinger","year":"2015","journal-title":"Version"},{"key":"2026090420313290900_btag638-B16","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1093\/bioinformatics\/btaa1092","article-title":"Recognition of small molecule\u2013RNA binding sites using RNA sequence and structure","volume":"37","author":"Su","year":"2021","journal-title":"Bioinformatics"},{"key":"2026090420313290900_btag638-B17","doi-asserted-by":"crossref","first-page":"btaf447","DOI":"10.1093\/bioinformatics\/btaf447","article-title":"RNA language model and graph attention network for RNA and small molecule binding sites prediction","volume":"41","author":"Sun","year":"2025","journal-title":"Bioinformatics"},{"key":"2026090420313290900_btag638-B18","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"2026090420313290900_btag638-B19","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1093\/bioinformatics\/bty345","article-title":"RBind: computational network method to predict RNA binding sites","volume":"34","author":"Wang","year":"2018","journal-title":"Bioinformatics"},{"key":"2026090420313290900_btag638-B20","doi-asserted-by":"crossref","first-page":"bbac486","DOI":"10.1093\/bib\/bbac486","article-title":"RLBind: a deep learning method to predict RNA\u2013ligand binding sites","volume":"24","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2026090420313290900_btag638-B21","doi-asserted-by":"crossref","first-page":"10076","DOI":"10.1038\/s41467-025-64972-0","article-title":"ERNIE-RNA: an RNA language model with structure-enhanced representations","volume":"16","author":"Yin","year":"2025","journal-title":"Nat Commun"},{"key":"2026090420313290900_btag638-B22","doi-asserted-by":"crossref","first-page":"169010","DOI":"10.1016\/j.jmb.2025.169010","article-title":"Identifying RNA-small molecule binding sites using geometric deep learning with language models","volume":"437","author":"Zhu","year":"2025","journal-title":"J Mol Biol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag638\/70765553\/btag638.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/9\/btag638\/70765553\/btag638.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/9\/btag638\/70765553\/btag638.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T00:31:48Z","timestamp":1788568308000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag638\/8768877"}},"subtitle":[],"editor":[{"given":"Jianlin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,8,22]]},"references-count":22,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,8,31]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag638","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,9]]},"published":{"date-parts":[[2026,8,22]]},"article-number":"btag638"}}