{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:18:46Z","timestamp":1781601526667,"version":"3.54.5"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"vor","delay-in-days":23,"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":["62225109"],"award-info":[{"award-number":["62225109"]}],"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":["62450112"],"award-info":[{"award-number":["62450112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2021YFC2100101"],"award-info":[{"award-number":["2021YFC2100101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>As part of the drug repurposing process, it is imperative to predict the interactions between drugs and target proteins in an accurate and efficient manner. With the introduction of contrastive learning into drug-target prediction, the accuracy of drug repurposing will be further improved. However, a large part of DTI prediction methods based on deep learning either focus only on the structural features of proteins and drugs extracted using GNN or CNN, or focus only on their relational features extracted using heterogeneous graph neural networks on a DTI heterogeneous graph. Since the structural and relational features of proteins and drugs describe their attribute information from different perspectives, their combination can improve DTI prediction performance. We propose a relational similarity-based graph contrastive learning for DTI prediction (RSGCL-DTI), which combines the structural and relational features of drugs and proteins to enhance the accuracy of DTI predictions. In our proposed method, the inter-protein relational features and inter-drug relational features are extracted from the heterogeneous drug\u2013protein interaction network through graph contrastive learning, respectively. The results demonstrate that combining the relational features obtained by graph contrastive learning with the structural ones extracted by D-MPNN and CNN enhances feature representation ability, thereby improving DTI prediction performance. Our proposed RSGCL-DTI outperforms eight SOTA baseline models on the four benchmark datasets, performs well on the imbalanced dataset, and also shows excellent generalization ability on unseen drug\u2013protein pairs.<\/jats:p>","DOI":"10.1093\/bib\/bbaf122","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T04:35:06Z","timestamp":1743222906000},"source":"Crossref","is-referenced-by-count":6,"title":["Relational similarity-based graph contrastive learning for DTI prediction"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5336-8763","authenticated-orcid":false,"given":"Jilong","family":"Bian","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040, Heilongjiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040, Heilongjiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limin","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040, Heilongjiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0403-7287","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040, Heilongjiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7381-2374","authenticated-orcid":false,"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040, Heilongjiang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"2025032422244262100_ref1","doi-asserted-by":"crossref","first-page":"bbae337","DOI":"10.1093\/bib\/bbae337","article-title":"MiRAGE: mining relationships for advanced generative evaluation in drug repositioning","volume":"25","author":"Aragh","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref2","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/nrd1468","article-title":"Drug repositioning: identifying and developing new uses for existing drugs","volume":"3","author":"Ashburn","year":"2004","journal-title":"Nat Rev Drug Discov"},{"key":"2025032422244262100_ref3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ymeth.2021.10.007","article-title":"Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions","volume":"198","author":"Jung","year":"2022","journal-title":"Methods"},{"key":"2025032422244262100_ref4","doi-asserted-by":"crossref","first-page":"vbad110","DOI":"10.1093\/bioadv\/vbad110","article-title":"DPSP: a multimodal deep learning framework for polypharmacy side effects prediction","volume":"3","author":"Masumshah","year":"2023","journal-title":"Bioinform Adv"},{"key":"2025032422244262100_ref5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04298-y","article-title":"A neural network-based method for polypharmacy side effects prediction","volume":"22","author":"Masumshah","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2025032422244262100_ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-3518-6","article-title":"Drug-target interaction prediction using semi-bipartite graph model and deep learning","volume":"21","author":"Manoochehri","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"2025032422244262100_ref7","doi-asserted-by":"publisher","first-page":"2397","DOI":"10.1093\/bioinformatics\/btp433","article-title":"Supervised prediction of drug-target interactions using bipartite local models","volume":"25","author":"Bleakley","year":"2009","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref8","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/bib\/bbu010","article-title":"Toward more realistic drug-target interaction predictions","volume":"16","author":"Tapio","year":"2015","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref9","doi-asserted-by":"publisher","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug-target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref10","doi-asserted-by":"publisher","first-page":"3582","DOI":"10.1093\/bioinformatics\/btac377","article-title":"Effective drug\u2013target interaction prediction with mutual interaction neural network","volume":"38","author":"Li","year":"2022","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref11","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1093\/bioinformatics\/btac155","article-title":"BridgeDPI: a novel graph neural network for predicting drug\u2013protein interactions","volume":"38","author":"Yifan","year":"2022","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref12","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab346","article-title":"Drug-target interaction predication via multi-channel graph neural networks","volume":"23","author":"Li","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref13","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae293","article-title":"Hierarchical multimodal self-attention-based graph neural network for DTI prediction","volume":"25","author":"Bian","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref14","doi-asserted-by":"publisher","first-page":"e1007129","DOI":"10.1371\/journal.pcbi.1007129","article-title":"DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences","volume":"15","author":"Lee","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2025032422244262100_ref15","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/978-3-030-01418-6_11","article-title":"DTI-RCNN: new efficient hybrid neural network model to predict drug\u2013target interactions","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN","author":"Zheng"},{"key":"2025032422244262100_ref16","first-page":"230","article-title":"Self-attention based molecule representation for predicting drug-target interaction","volume-title":"Machine Learning for Healthcare Conference","author":"Shin","year":"2019"},{"key":"2025032422244262100_ref17","article-title":"Attention is all you need","volume":"30","author":"Vaswani","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2025032422244262100_ref18","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1089\/cmb.2017.0135","article-title":"A computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network","volume":"25","author":"Wang","year":"2018","journal-title":"J Comput Biol"},{"key":"2025032422244262100_ref19","doi-asserted-by":"crossref","first-page":"bbac272","DOI":"10.1093\/bib\/bbac272","article-title":"AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification","volume":"23","author":"Yazdani-Jahromi","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref20","article-title":"Topology adaptive graph convolutional networks","author":"Jian","year":"2018"},{"key":"2025032422244262100_ref21","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"key":"2025032422244262100_ref22","doi-asserted-by":"publisher","first-page":"4485","DOI":"10.1093\/bioinformatics\/btab473","article-title":"MultiDTI: drug\u2013target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network","volume":"37","author":"Zhou","year":"2021","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref23","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa430","article-title":"An end-to-end heterogeneous graph representation learning-based framework for drug\u2013target interaction prediction","volume":"22","author":"Peng","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref24","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1109\/JBHI.2022.3217433","article-title":"PPAEDTI: personalized propagation auto-encoder model for predicting drug-target interactions","volume":"27","author":"Li","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025032422244262100_ref25","doi-asserted-by":"publisher","first-page":"3186","DOI":"10.1021\/jm401411z","article-title":"Molecular similarity in medicinal chemistry: miniperspective","volume":"57","author":"Maggiora","year":"2014","journal-title":"J Med Chem"},{"key":"2025032422244262100_ref26","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2025032422244262100_ref27","doi-asserted-by":"crossref","first-page":"9726","DOI":"10.1109\/CVPR42600.2020.00975","article-title":"Momentum contrast for unsupervised visual representation learning","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He","year":"2020"},{"key":"2025032422244262100_ref28","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"33","author":"Grill","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2025032422244262100_ref29","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"In International Conference on Machine Learning","author":"Chen"},{"key":"2025032422244262100_ref30","first-page":"1909","article-title":"Contrastive graph structure learning via information bottleneck for recommendation","volume":"35","author":"Wei","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"key":"2025032422244262100_ref31","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1145\/3534678.3539229","article-title":"CrossCBR: cross-view contrastive learning for bundle recommendation","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Ma","year":"2022"},{"key":"2025032422244262100_ref32","article-title":"Bootstrapped representation learning on graphs","volume-title":"ICLR 2021 Workshop on Geometrical and Topological Representation Learning","author":"Thakoor"},{"key":"2025032422244262100_ref33","doi-asserted-by":"publisher","first-page":"2847","DOI":"10.1093\/bioinformatics\/btac164","article-title":"Supervised graph co-contrastive learning for drug-target interaction prediction","volume":"38","author":"Li","year":"2022","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref34","doi-asserted-by":"crossref","DOI":"10.1101\/676825","article-title":"Evaluating protein transfer learning with TAPE","volume-title":"Advances in Neural Information Processing Systems","author":"Rao","year":"2019"},{"key":"2025032422244262100_ref35","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1093\/bioinformatics\/btab715","article-title":"HyperAttentionDTI: improving drug\u2013protein interaction prediction by sequence-based deep learning with attention mechanism","volume":"38","author":"Zhao","year":"2022","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref36","article-title":"BioSNAP datasets: Stanford biomedical network dataset collection","author":"Zitnik","year":"2018"},{"key":"2025032422244262100_ref37","doi-asserted-by":"publisher","first-page":"i221","DOI":"10.1093\/bioinformatics\/btv256","article-title":"Improving compound-protein interaction prediction by building up highly credible negative samples","volume":"31","author":"Hui","year":"2015","journal-title":"Bioinformatics"},{"key":"2025032422244262100_ref38","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.1109\/TCBB.2021.3077905","article-title":"Drug-target interaction prediction using multi-head self-attention and graph attention network","volume":"19","author":"Cheng","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025032422244262100_ref39","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TCBB.2022.3225423","article-title":"GIFDTI: prediction of drug-target interactions based on global molecular and intermolecular interaction representation learning","volume":"20","author":"Zhao","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025032422244262100_ref40","doi-asserted-by":"publisher","first-page":"bbad082","DOI":"10.1093\/bib\/bbad082","article-title":"MCANet: shared-weight-based multiheadcrossattention network for drug-target interaction prediction","volume":"24","author":"Bian","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref41","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3492806","article-title":"BINDTI: a bi-directional intention network for drug-target interaction identification based on attention mechanisms","volume":"29","author":"Peng","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025032422244262100_ref42","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1186\/s12859-023-05447-1","article-title":"MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug\u2013target interaction","volume":"24","author":"Qian","year":"2023","journal-title":"BMC Bioinformatics"},{"key":"2025032422244262100_ref43","doi-asserted-by":"publisher","first-page":"bbac446","DOI":"10.1093\/bib\/bbac446","article-title":"CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation","volume":"23","author":"Huang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025032422244262100_ref44","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/JBHI.2024.3372527","article-title":"DRGCL: drug repositioning via semantic-enriched graph contrastive learning","volume":"29","author":"Jia","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025032422244262100_ref45","first-page":"bbad474","article-title":"AMGDTI: drug\u2013target interaction prediction based on adaptive meta-graph learning in heterogeneous network","volume":"25","author":"Yansen","year":"2024","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf122\/62572419\/bbaf122.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf122\/62572419\/bbaf122.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T04:35:58Z","timestamp":1743222958000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf122\/8092301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf122","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,3]]},"published":{"date-parts":[[2025,3]]},"article-number":"bbaf122"}}