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QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/data.snu.ac.kr\/QSAR\/\">http:\/\/data.snu.ac.kr\/QSAR\/<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-019-3135-4","type":"journal-article","created":{"date-parts":[[2019,10,26]],"date-time":"2019-10-26T14:04:36Z","timestamp":1572098676000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":206,"title":["Comprehensive ensemble in QSAR prediction for drug discovery"],"prefix":"10.1186","volume":"20","author":[{"given":"Sunyoung","family":"Kwon","sequence":"first","affiliation":[]},{"given":"Ho","family":"Bae","sequence":"additional","affiliation":[]},{"given":"Jeonghee","family":"Jo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2367-197X","authenticated-orcid":false,"given":"Sungroh","family":"Yoon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,26]]},"reference":[{"issue":"1","key":"3135_CR1","doi-asserted-by":"publisher","first-page":"95","DOI":"10.2174\/156802610790232260","volume":"10","author":"J Verma","year":"2010","unstructured":"Verma J, Khedkar VM, Coutinho EC. 3d-qsar in drug design-a review. 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