{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:06:11Z","timestamp":1780761971687,"version":"3.54.1"},"reference-count":74,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":46,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec>\n                    <jats:label\/>\n                    <jats:p>Computational methods for DDIs and DTIs prediction are essential for accelerating the drug discovery process. We proposed a novel deep learning method DeepDrug, to tackle these two problems within a unified framework. DeepDrug is capable of extracting comprehensive features of both drug and target protein, thus demonstrating a superior prediction performance in a series of experiments. The downstream applications show that DeepDrug is useful in facilitating drug repositioning and discovering the potential drug against specific disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Computational approaches for accurate prediction of drug interactions, such as drug\u2010drug interactions (DDIs) and drug\u2010target interactions (DTIs), are highly demanded for biochemical researchers. Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively, their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this paper, we develop DeepDrug, a deep learning framework for overcoming the above limitation by using residual graph convolutional networks (Res\u2010GCNs) and convolutional networks (CNNs) to learn the comprehensive structure\u2010 and sequence\u2010based representations of drugs and proteins.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DeepDrug outperforms state\u2010of\u2010the\u2010art methods in a series of systematic experiments, including binary\u2010class DDIs, multi\u2010class\/multi\u2010label DDIs, binary\u2010class DTIs classification and DTIs regression tasks. Furthermore, we visualize the structural features learned by DeepDrug Res\u2010GCN module, which displays compatible and accordant patterns in chemical properties and drug categories, providing additional evidence to support the strong predictive power of DeepDrug. Ultimately, we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS\u2010CoV\u20102, where 7 out of 10 top\u2010ranked drugs are reported to be repurposed to potentially treat coronavirus disease 2019 (COVID\u201019).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>To sum up, we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.15302\/j-qb-022-0320","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T04:27:02Z","timestamp":1679459222000},"page":"260-274","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["DeepDrug: A general graph\u2010based deep learning framework for drug\u2010drug interactions and drug\u2010target interactions prediction"],"prefix":"10.1002","volume":"11","author":[{"given":"Qijin","family":"Yin","sequence":"first","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology Center for Synthetic and Systems Biology Department of Automation Tsinghua University  Beijing 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Software Nankai University  Tianjin 300350 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xusheng","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Software Nankai University  Tianjin 300350 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics Stanford University  Stanford CA 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology Center for Synthetic and Systems Biology Department of Automation Tsinghua University  Beijing 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanwen","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Statistics Stanford University  Stanford CA 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp433"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.09.012"},{"key":"e_1_2_10_4_1","first-page":"47","article-title":"Sildenafil: an orally active type 5 cyclic GMP\u2010specific phosphodiesterase inhibitor for the treatment of penile erectile dysfunction","volume":"8","author":"Boolell M.","year":"1996","journal-title":"Int. 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