{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T13:46:59Z","timestamp":1784036819637,"version":"3.55.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"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"}]},{"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":["62072095"],"award-info":[{"award-number":["62072095"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug\u2013target interactions (DTIs) are a key part of drug development process and their accurate and efficient prediction can significantly boost development efficiency and reduce development time. Recent years have witnessed the rapid advancement of deep learning, resulting in an abundance of deep learning-based models for DTI prediction. However, most of these models used a single representation of drugs and proteins, making it difficult to comprehensively represent their characteristics. Multimodal data fusion can effectively compensate for the limitations of single-modal data. However, existing multimodal models for DTI prediction do not take into account both intra- and inter-modal interactions simultaneously, resulting in limited presentation capabilities of fused features and a reduction in DTI prediction accuracy. A hierarchical multimodal self-attention-based graph neural network for DTI prediction, called HMSA-DTI, is proposed to address multimodal feature fusion. Our proposed HMSA-DTI takes drug SMILES, drug molecular graphs, protein sequences and protein 2-mer sequences as inputs, and utilizes a hierarchical multimodal self-attention mechanism to achieve deep fusion of multimodal features of drugs and proteins, enabling the capture of intra- and inter-modal interactions between drugs and proteins. It is demonstrated that our proposed HMSA-DTI has significant advantages over other baseline methods on multiple evaluation metrics across five benchmark datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbae293","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T11:41:42Z","timestamp":1719402102000},"source":"Crossref","is-referenced-by-count":25,"title":["Hierarchical multimodal self-attention-based graph neural network for DTI prediction"],"prefix":"10.1093","volume":"25","author":[{"given":"Jilong","family":"Bian","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghui","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"2024062611080507800_ref1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/bib\/bbz157","article-title":"Machine learning approaches and databases for prediction of drug-target interaction: a survey paper","volume":"22","author":"Bagherian","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024062611080507800_ref2","article-title":"Drug-target interaction predication via multi-channel graph neural networks","volume":"23","author":"Li","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024062611080507800_ref3","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1002\/minf.201400009","article-title":"Computational prediction of drug-target interactions using chemical, biological, and network features","volume":"33","author":"Cao","year":"2014","journal-title":"Molecular Informatics"},{"key":"2024062611080507800_ref4","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1016\/j.ygeno.2018.12.007","article-title":"Predicting drug-target interactions using lasso with random forest based on evolutionary information and chemical structure","volume":"111","author":"Shi","year":"2019","journal-title":"Genomics"},{"key":"2024062611080507800_ref5","first-page":"1","article-title":"Random-forest model for drug-target interaction prediction via kullback-leibler divergence","volume":"14","author":"Ahn","year":"2022","journal-title":"J Chem"},{"key":"2024062611080507800_ref6","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1093\/bioinformatics\/btn409","article-title":"Protein-ligand interaction prediction: an improved chemogenomics approach","volume":"24","author":"Jacob","year":"2008","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref7","doi-asserted-by":"crossref","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":"2024062611080507800_ref8","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0171839","article-title":"Self-blm: prediction of drug-target interactions via self-training svm","volume":"12","author":"Keum","year":"2017","journal-title":"PloS One"},{"key":"2024062611080507800_ref9","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1089\/cmb.2010.0213","article-title":"Combining drug and gene similarity measures for drug-target elucidation","volume":"18","author":"Perlman","year":"2011","journal-title":"J Comput Biol"},{"key":"2024062611080507800_ref10","article-title":"Predicting drug-target interactions using drug-drug interactions","volume":"8","author":"Kim","year":"2013","journal-title":"PloS One"},{"key":"2024062611080507800_ref11","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1109\/TCBB.2020.2968025","article-title":"Negstacking: drug-target interaction prediction based on ensemble learning and logistic regression","volume":"18","author":"Yang","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024062611080507800_ref12","doi-asserted-by":"crossref","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":"2024062611080507800_ref13","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"Deepdta: deep drug-target binding affinity prediction","volume":"34","author":"\u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref14","doi-asserted-by":"crossref","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":"2024062611080507800_ref15","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1093\/bioinformatics\/btaa544","article-title":"Deepcda: deep cross-domain compound-protein affinity prediction through lstm and convolutional neural networks","volume":"36","author":"Abbasi","year":"2020","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref16","first-page":"5990","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2024062611080507800_ref17","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1093\/bioinformatics\/btaa524","article-title":"Transformercpi: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref18","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","article-title":"Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences","volume":"35","author":"Tsubaki","year":"2018","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref19","doi-asserted-by":"crossref","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":"2024062611080507800_ref20","doi-asserted-by":"crossref","first-page":"643","DOI":"10.3390\/biom11050643","article-title":"Csconv2d: a 2-d structural convolution neural network with a channel and spatial attention mechanism for protein-ligand binding affinity prediction","volume":"11","author":"Wang","year":"2021","journal-title":"Biomolecules"},{"key":"2024062611080507800_ref21","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1039\/C9SC03414E","article-title":"Deepscreen: high performance drug\u2013target interaction prediction with convolutional neural networks using 2-d structural compound representations","volume":"11","author":"Rifaioglu","year":"2020","journal-title":"Chem Sci"},{"key":"2024062611080507800_ref22","doi-asserted-by":"crossref","first-page":"4131","DOI":"10.1021\/acs.jcim.9b00628","article-title":"Graph convolutional neural networks for predicting drug-target interactions","volume":"59","author":"Torng","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2024062611080507800_ref23","first-page":"126","article-title":"Interpretable bilinear attention network with domain adaptation improves drug\u2013target prediction. Nature","volume":"5","author":"Bai","year":"2022","journal-title":"Machine Intelligence"},{"key":"2024062611080507800_ref24","first-page":"10944","article-title":"What makes multi-modal learning better than single (provably)","volume":"34","author":"Huang","year":"2021","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"2024062611080507800_ref25","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1093\/bioinformatics\/btac155","article-title":"Bridgedpi: a novel graph neural network for predicting drug-protein interactions","volume":"38","author":"Yifan","year":"2022","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref26","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/BIBM55620.2022.9995411","article-title":"Predicting compound-protein interaction by deepening the systemic background via molecular network feature embedding","volume-title":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Wang","year":"2022"},{"key":"2024062611080507800_ref27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCBB.2022.3144008","article-title":"Cpinformer for efficient and robust compound-protein interaction prediction","volume":"20","author":"Hua","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024062611080507800_ref28","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhou","year":"2021"},{"key":"2024062611080507800_ref29","doi-asserted-by":"crossref","first-page":"i92","DOI":"10.1093\/bioinformatics\/btx234","article-title":"Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding","volume":"33","author":"Min","year":"2017","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref30","first-page":"9686","article-title":"Evaluating protein transfer learning with tape","volume":"32","author":"Rao","year":"2019","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"2024062611080507800_ref31","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1093\/bioinformatics\/btab715","article-title":"Hyperattentiondti: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism","volume":"38","author":"Zhao","year":"2022","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref32","doi-asserted-by":"crossref","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":"2024062611080507800_ref33","doi-asserted-by":"crossref","first-page":"8693","DOI":"10.1039\/D2SC02023H","article-title":"Learning size-adaptive molecular substructures for explainable drug\u2013drug interaction prediction by substructure-aware graph neural network","volume":"13","author":"Yang","year":"2022","journal-title":"Chem Sci"},{"key":"2024062611080507800_ref34","doi-asserted-by":"crossref","first-page":"bbab569","DOI":"10.1093\/bib\/bbab569","article-title":"Multimodal deep learning for biomedical data fusion: a review","volume":"23","author":"Stahlschmidt","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024062611080507800_ref35","doi-asserted-by":"crossref","first-page":"i221","DOI":"10.1093\/bioinformatics\/btv256","article-title":"Improving compound\u2013protein interaction prediction by building up highly credible negative samples","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"key":"2024062611080507800_ref36","volume-title":"Biosnap datasets: Stanford biomedical network dataset collection","author":"Zitnik","year":"2018"},{"key":"2024062611080507800_ref37","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nat Biotechnol"},{"key":"2024062611080507800_ref38","doi-asserted-by":"crossref","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":"2024062611080507800_ref39","doi-asserted-by":"crossref","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":"2024062611080507800_ref40","doi-asserted-by":"crossref","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":"2024062611080507800_ref41","doi-asserted-by":"crossref","first-page":"7794","DOI":"10.1109\/CVPR.2018.00813","article-title":"Non-local neural networks","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang","year":"2018"},{"key":"2024062611080507800_ref42","doi-asserted-by":"crossref","first-page":"27177","DOI":"10.1007\/s10489-023-04977-8","article-title":"Csdti: an interpretable cross-attention network with gnn-based drug molecule aggregation for drug-target interaction prediction","volume":"53","author":"Yaohua","year":"2023","journal-title":"Applied Intelligence"},{"key":"2024062611080507800_ref43","first-page":"3438","article-title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","author":"Chen","year":"2020"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/4\/bbae293\/58336796\/bbae293.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/4\/bbae293\/58336796\/bbae293.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T11:42:44Z","timestamp":1719402164000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae293\/7699346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,5,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae293","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,7]]},"published":{"date-parts":[[2024,5,23]]},"article-number":"bbae293"}}