{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T21:20:19Z","timestamp":1726435219720},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"Abstract<\/jats:title>\n Precisely predicting the drug\u2013drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.<\/jats:p>","DOI":"10.1093\/bib\/bbac576","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T15:09:46Z","timestamp":1669648186000},"source":"Crossref","is-referenced-by-count":5,"title":["R2-DDI: relation-aware feature refinement for drug\u2013drug interaction prediction"],"prefix":"10.1093","volume":"24","author":[{"given":"Jiacheng","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Automation, Tsinghua University , 30 Shuangqing Rd, Haidian District, 100084 Beijing , China"}]},{"given":"Lijun","family":"Wu","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , No. 5 Dan Ling Street, Haidian District, 100080 Beijing , China"}]},{"given":"Jinhua","family":"Zhu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of GIPAS, EEIS Department, University of Science and Technology of China , No. 96, JinZhai Road Baohe District, 230026 Hefei, Anhui Province , China"}]},{"given":"Xiaobo","family":"Liang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Soochow University , No. 178, Yucai Rd, Gusu District, 215006 Soochow, Jaingsu Province , China"}]},{"given":"Yingce","family":"Xia","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , No. 5 Dan Ling Street, Haidian District, 100080 Beijing , China"}]},{"given":"Shufang","family":"Xie","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , No. 5 Dan Ling Street, Haidian District, 100080 Beijing , China"}]},{"given":"Tao","family":"Qin","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , No. 5 Dan Ling Street, Haidian District, 100080 Beijing , China"}]},{"given":"Tie-Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , No. 5 Dan Ling Street, Haidian District, 100080 Beijing , China"}]}],"member":"286","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"issue":"7","key":"2023011917103169100_ref1","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nbt.2284","article-title":"Combinatorial drug therapy for cancer in the post-genomic era","volume":"30","author":"Al-Lazikani","year":"2012","journal-title":"Nat Biotechnol"},{"issue":"3","key":"2023011917103169100_ref2","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1128\/CMR.05041-11","article-title":"Combination therapy for treatment of infections with gram-negative bacteria","volume":"25","author":"Tamma","year":"2012","journal-title":"Clin Microbiol 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