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Therefore, in this study, using the chemical structure information of SCAR\u2010causative drugs from the Japanese Adverse Drug Event Report (JADER) database, we tried to develop a predictive classification model of SCAR through deep learning. In the JADER database from 2004 to 2017, we defined 185 SCAR\u2010positive drugs and 195 SCAR\u2010negative drugs using proportional reporting ratios as the signal detection method, and the total number of reports. These SCAR\u2010positive and SCAR\u2010negative drugs were randomly divided into the training dataset for model construction and the test dataset for evaluation. The model performance was evaluated in the independent test dataset inside the applicability domain (AD), which is the chemical space for reliable prediction results. Using the deep learning model with molecular descriptors as the drug structure information, the area under the curve was 0.76 for the 148 drugs of the test dataset inside the AD. The method developed in the present study allows for utilizing the JADER database for SCAR classification, with potential to improve screening efficiency in the development of new drugs. This method may also help to noninvasively identify the causative drug, and help assess the causality between drugs and SCARs in postmarketing surveillance.<\/jats:p>","DOI":"10.1111\/cts.12944","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T18:57:57Z","timestamp":1610564277000},"page":"756-763","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["<i>In Silico<\/i> Approach to Predict Severe Cutaneous Adverse Reactions Using the Japanese Adverse Drug Event Report Database"],"prefix":"10.1111","volume":"14","author":[{"given":"Kaori","family":"Ambe","sequence":"first","affiliation":[{"name":"Department of Regulatory Science Graduate School of Pharmaceutical Sciences Nagoya City University  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuyuki","family":"Ohya","sequence":"additional","affiliation":[{"name":"Department of Regulatory Science Graduate School of Pharmaceutical Sciences Nagoya City University  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waki","family":"Takada","sequence":"additional","affiliation":[{"name":"Department of Regulatory Science Graduate School of Pharmaceutical Sciences Nagoya City University  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaharu","family":"Suzuki","sequence":"additional","affiliation":[{"name":"Department of Regulatory Science Graduate School of Pharmaceutical Sciences Nagoya City University  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Tohkin","sequence":"additional","affiliation":[{"name":"Department of Regulatory Science Graduate School of Pharmaceutical Sciences Nagoya City University  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"e_1_2_10_1_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e31821201ed"},{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12016-017-8654-z"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199512143332404"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.1071"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1085-5629(96)80038-1"},{"key":"e_1_2_10_6_1","first-page":"139","article-title":"Drug reaction with eosinophilia and systemic symptoms (DRESS): incidence, pathogenesis and management","volume":"16","author":"Shiohara T.","year":"2017","journal-title":"Expert Opin. 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