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Network-based methods usually utilize a drug\u2013protein association network and predict DPIs by the information of its associated proteins or drugs, called \u2018guilt-by-association\u2019 principle. However, the \u2018guilt-by-association\u2019 principle is not always true because sometimes similar proteins cannot interact with similar drugs. Recently, learning-based methods learn molecule properties underlying DPIs by utilizing existing databases of characterized interactions but neglect the network-level information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose a novel method, namely BridgeDPI. We devise a class of virtual nodes to bridge the gap between drugs and proteins and construct a learnable drug\u2013protein association network. The network is optimized based on the supervised signals from the downstream task\u2014the DPI prediction. Through information passing on this drug\u2013protein association network, a Graph Neural Network can capture the network-level information among diverse drugs and proteins. By combining the network-level information and the learning-based method, BridgeDPI achieves significant improvement in three real-world DPI datasets. Moreover, the case study further verifies the effectiveness and reliability of BridgeDPI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source code of BridgeDPI can be accessed at https:\/\/github.com\/SenseTime-Knowledge-Mining\/BridgeDPI. The source data used in this study is available on the https:\/\/github.com\/IBM\/InterpretableDTIP (for the BindingDB dataset), https:\/\/github.com\/masashitsubaki\/CPI_prediction (for the C.ELEGANS and HUMAN) datasets, http:\/\/dude.docking.org\/ (for the DUD-E dataset), repectively.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac155","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:11:29Z","timestamp":1646943089000},"page":"2571-2578","source":"Crossref","is-referenced-by-count":110,"title":["BridgeDPI: a novel Graph Neural Network for predicting drug\u2013protein interactions"],"prefix":"10.1093","volume":"38","author":[{"given":"Yifan","family":"Wu","sequence":"first","affiliation":[{"name":"SenseTime Research , Shanghai 200233, China"},{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Gao","sequence":"additional","affiliation":[{"name":"SenseTime Research , Shanghai 200233, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8264-5950","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"SenseTime Research , Shanghai 200233, China"},{"name":"Qing Yuan Research Institute, Shanghai Jiao Tong University , Shanghai 200240, China"},{"name":"Merck Advisory Committee for AI-enabled Health Solution , Shanghai 200126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"2023041402570502600_","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1056\/NEJMp1500848","article-title":"The $2.6 billion pill\u2013methodologic and policy considerations","volume":"372","author":"Avorn","year":"2015","journal-title":"N. 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