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Heterogeneous data sources can provide comprehensive information and different perspectives for drug\u2013target interaction prediction. Thus, there have been many calculation methods relying on heterogeneous networks. Most of them use graph-related algorithms to characterize nodes in heterogeneous networks for predicting new drug\u2013target interactions (DTI). However, these methods can only make predictions in known heterogeneous network datasets, and cannot support the prediction of new chemical entities outside the heterogeneous network, which hinder further drug discovery and development.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To solve this problem, we proposed a multi-modal DTI prediction model named \u2018MultiDTI\u2019 which uses our proposed joint learning framework based on heterogeneous networks. It combines the interaction or association information of the heterogeneous network and the drug\/target sequence information, and maps the drugs, targets, side effects and disease nodes in the heterogeneous network into a common space. In this way, \u2018MultiDTI\u2019 can map the new chemical entity to this learned common space based on the chemical structure of the new entity. That is, bridging the gap between new chemical entities and known heterogeneous network. Our model has strong predictive performance, and the area under the receiver operating characteristic curve of the model is 0.961 and the area under the precision recall curve is 0.947 with 10-fold cross validation. In addition, some predicted new DTIs have been confirmed by ChEMBL database. Our results indicate that \u2018MultiDTI\u2019 is a powerful and practical tool for predicting new DTI, which can promote the development of drug discovery or drug repositioning.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Python codes and dataset are available at https:\/\/github.com\/Deshan-Zhou\/MultiDTI\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab473","type":"journal-article","created":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T11:07:39Z","timestamp":1624792059000},"page":"4485-4492","source":"Crossref","is-referenced-by-count":95,"title":["MultiDTI: drug\u2013target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6725-9303","authenticated-orcid":false,"given":"Deshan","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hunan University , Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-8473","authenticated-orcid":false,"given":"Zhijian","family":"Xu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WenTao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Defense Technology , Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolan","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Guilin University of Technology , Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoliang","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hunan University , Changsha 410082, China"},{"name":"Department of Computer Science, National University of Defense Technology , Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"2023061310490478700_btab473-B1","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":"2023061310490478700_btab473-B2","doi-asserted-by":"crossref","first-page":"D945","DOI":"10.1093\/nar\/gkw1074","article-title":"The ChEMBL database in 2017","volume":"45","author":"Anna","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023061310490478700_btab473-B3","first-page":"302","author":"Bahi","year":"2018"},{"key":"2023061310490478700_btab473-B4","doi-asserted-by":"crossref","first-page":"106114","DOI":"10.1016\/j.knosys.2020.106114","article-title":"Hashtag our stories: hashtag recommendation for micro-videos via harnessing multiple modalities","volume":"203","author":"Cao","year":"2020","journal-title":"Knowledge Based Syst"},{"key":"2023061310490478700_btab473-B5","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 (Oxford, England)"},{"key":"2023061310490478700_btab473-B6","doi-asserted-by":"crossref","first-page":"e1002503","DOI":"10.1371\/journal.pcbi.1002503","article-title":"Prediction of drug\u2013target interactions and drug repositioning via network-based inference","volume":"8","author":"Cheng","year":"2012","journal-title":"PLoS Comput. 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