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Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-12-157","type":"journal-article","created":{"date-parts":[[2011,5,14]],"date-time":"2011-05-14T18:17:41Z","timestamp":1305397061000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Discrimination of approved drugs from experimental drugs by learning methods"],"prefix":"10.1186","volume":"12","author":[{"given":"Kailin","family":"Tang","sequence":"first","affiliation":[]},{"given":"Ruixin","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yixue","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhiwei","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,5,14]]},"reference":[{"issue":"9","key":"4563_CR1","first-page":"34","volume":"29","author":"D Anson Blake","year":"2009","unstructured":"Anson Blake D, Junyi Ma, Jia-Qiang He: Identifying Cardiotoxic Compounds. 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