{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:07:51Z","timestamp":1783436871387,"version":"3.54.6"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"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":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Drug\u2013drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.<\/jats:p>","DOI":"10.1093\/bib\/bbad041","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:23:08Z","timestamp":1675815788000},"source":"Crossref","is-referenced-by-count":17,"title":["Improving drug\u2013drug interactions prediction with interpretability via meta-path-based information fusion"],"prefix":"10.1093","volume":"24","author":[{"given":"Weizhong","family":"Zhao","sequence":"first","affiliation":[{"name":"Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P R China"},{"name":"Beijing University of Posts and Telecommunications School of Computer Science, , Beijing, 100876, P R China"},{"name":"National Language Resources Monitoring & Research Center for Network Media, Central China Normal University , Wuhan, Hubei 430079, P R China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueling","family":"Yuan","sequence":"additional","affiliation":[{"name":"Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P R China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianjun","family":"Shen","sequence":"additional","affiliation":[{"name":"Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P R China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingpeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P R China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications , Beijing 100876, P R China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"He","sequence":"additional","affiliation":[{"name":"Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P R China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computing & Informatics, Drexel University , Philadelphia, PA 19104 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"2023032004455713200_","volume-title":"Stockley\u2019s Drug Interactions","author":"Baxter","year":"2010"},{"key":"2023032004455713200_","article-title":"A comprehensive review of computational methods for drug-drug interaction 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