{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:32:12Z","timestamp":1774488732759,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20321"],"award-info":[{"award-number":["U23A20321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272490"],"award-info":[{"award-number":["62272490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the discovery of such drugs, it is essential to develop an effective computational method for predicting RNA\u2013small molecule affinity (RSMA). Recently, deep learning-based computational methods have been promising due to their powerful nonlinear modeling ability. However, the leveraging of advanced deep learning methods to mine the diverse information of RNAs, small molecules, and their interaction still remains a great challenge.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we present DeepRSMA, an innovative cross-attention-based deep learning method for RSMA prediction. To effectively capture fine-grained features from RNA and small molecules, we developed nucleotide-level and atomic-level feature extraction modules for RNA and small molecules, respectively. Additionally, we incorporated both sequence and graph views into these modules to capture features from multiple perspectives. Moreover, a transformer-based cross-fusion module is introduced to learn the general patterns of interactions between RNAs and small molecules. To achieve effective RSMA prediction, we integrated the RNA and small molecule representations from the feature extraction and cross-fusion modules. Our results show that DeepRSMA outperforms baseline methods in multiple test settings. The interpretability analysis and the case study on spinal muscular atrophy demonstrate that DeepRSMA has the potential to guide RNA-targeted drug design.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The codes and data are publicly available at https:\/\/github.com\/Hhhzj-7\/DeepRSMA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae678","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T13:47:23Z","timestamp":1731592043000},"source":"Crossref","is-referenced-by-count":14,"title":["DeepRSMA: a cross-fusion-based deep learning method for RNA\u2013small molecule binding affinity prediction"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1949-167X","authenticated-orcid":false,"given":"Zhijian","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}]},{"given":"Yucheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Machine Intellection Department, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR) , Singapore 138632,","place":["Singapore"]}]},{"given":"Song","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}]},{"given":"Yaw Sing","family":"Tan","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR) , Singapore 138671,","place":["Singapore"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2869-1619","authenticated-orcid":false,"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Machine Intellection Department, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR) , Singapore 138632,","place":["Singapore"]}]}],"member":"286","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"2024121522053556700_btae678-B1","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1093\/bioinformatics\/btaa544","article-title":"DeepCDA: deep cross-domain compound\u2013protein affinity prediction through LSTM and convolutional neural networks","volume":"36","author":"Abbasi","year":"2020","journal-title":"Bioinformatics"},{"key":"2024121522053556700_btae678-B3","doi-asserted-by":"crossref","first-page":"7262","DOI":"10.1021\/acs.jmedchem.2c00254","article-title":"Quantitative structure\u2013activity relationship (QSAR) study predicts 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