{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:06:27Z","timestamp":1770840387804,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["2021D01D05"],"award-info":[{"award-number":["2021D01D05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The task of predicting drug\u2013target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets.<\/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\/stevejobws\/iGRLDTI\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad451","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T12:45:50Z","timestamp":1690461950000},"source":"Crossref","is-referenced-by-count":89,"title":["iGRLDTI: an improved graph representation learning method for predicting drug\u2013target interactions over heterogeneous biological information network"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8200-6016","authenticated-orcid":false,"given":"Bo-Wei","family":"Zhao","sequence":"first","affiliation":[{"name":"The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences , Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing , Urumqi 830011, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5468-6085","authenticated-orcid":false,"given":"Xiao-Rui","family":"Su","sequence":"additional","affiliation":[{"name":"The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences , Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing , Urumqi 830011, China"}]},{"given":"Peng-Wei","family":"Hu","sequence":"additional","affiliation":[{"name":"The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences , Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing , Urumqi 830011, China"}]},{"given":"Yu-An","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710129, China"}]},{"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1591-8549","authenticated-orcid":false,"given":"Lun","family":"Hu","sequence":"additional","affiliation":[{"name":"The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences , 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