{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T06:34:38Z","timestamp":1774766078736,"version":"3.50.1"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100010083","name":"Hunan Provincial Innovation Foundation for Postgraduate","doi-asserted-by":"publisher","award":["CX20200434"],"award-info":[{"award-number":["CX20200434"]}],"id":[{"id":"10.13039\/501100010083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug\u2013drug similarity, disease\u2013disease similarity and drug\u2013disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer\u2019s disease (e.g. benzatropine) and Parkinson\u2019s disease (e.g. trihexyphenidyl and haloperidol).<\/jats:p>","DOI":"10.1093\/bib\/bbab319","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T11:09:14Z","timestamp":1627038554000},"source":"Crossref","is-referenced-by-count":157,"title":["Drug repositioning based on the heterogeneous information fusion graph convolutional network"],"prefix":"10.1093","volume":"22","author":[{"given":"Lijun","family":"Cai","sequence":"first","affiliation":[{"name":"Hunan University, Changsha, Hunan, 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9263-8463","authenticated-orcid":false,"given":"Changcheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, 410082, China"}]},{"given":"Junlin","family":"Xu","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, 410082, China"}]},{"given":"Yajie","family":"Meng","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, 410082, China"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, 410082, China"}]},{"given":"Xiangzheng","family":"Fu","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, 410082, China"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, 410082, China"}]},{"given":"Yansen","family":"Su","sequence":"additional","affiliation":[{"name":"Anhui University, Changsha, Hunan, 410082, 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