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Many computational methods have been proposed for this purpose, but most require specific types of information, or they have less concern in interpretation on underlying genes. We propose a deep learning-based framework for DDI prediction with drug-induced gene expression signatures so that the model can provide the expression level of interpretability for DDIs. The model engineers dynamic drug features using a gating mechanism that mimics the co-administration effects by imposing attention to genes. Also, each side-effect is projected into a latent space through translating embedding. As a result, the model achieved an AUC of 0.889 and an AUPR of 0.915 in unseen interaction prediction, which is competitively very accurate and outperforms other state-of-the-art methods. Furthermore, it can predict potential DDIs with new compounds not used in training. In conclusion, using drug-induced gene expression signatures followed by gating and translating embedding can increase DDI prediction accuracy while providing model interpretability. The source code is available on GitHub (https:\/\/github.com\/GIST-CSBL\/DeSIDE-DDI<\/jats:ext-link>).<\/jats:p>","DOI":"10.1186\/s13321-022-00589-5","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T13:26:31Z","timestamp":1646400391000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions"],"prefix":"10.1186","volume":"14","author":[{"given":"Eunyoung","family":"Kim","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5109-9114","authenticated-orcid":false,"given":"Hojung","family":"Nam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"issue":"12","key":"589_CR1","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1038\/nbt.3052","volume":"32","author":"M Bansal","year":"2014","unstructured":"Bansal M, Yang J, Karan C, Menden MP, Costello JC, Tang H et al (2014) A community computational challenge to predict the activity of pairs of compounds. 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