title: Discovering breast cancer drug candidates from biomedical literature.
abstract: We developed a new paradigm with the ultimate goal of enabling disease-specific drug candidate discovery with molecular-level evidences generated from literature and prior knowledge. We showed how to implement the paradigm by building a prototype literature-mining framework and performing drug-protein association mining for breast cancer drug discovery. In a molecular pharmacology study of breast cancer, 79.2% of 729 enriched drugs in 'Organic Chemicals' category were validated to be disease-related, and the remaining 20.8% were also investigated as potential for future molecular therapeutics studies. 'Doxorubicin', 'Etoposide' and 'Paclitaxel' were identified as having similar pharmacological profiles to treat breast cancer.
authors.affiliation: State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. jiao-li04@mails.tsinghua.edu.cn
authors.forename: Jiao
authors.initials: J
authors.lastname: Li
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Zhu
authors.forename: Jake Yue
authors.initials: JY
authors.lastname: Chen
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy*; D057225:Data Mining / Q000379:methods*; D004317:Doxorubicin / Q000627:therapeutic use; D055808:Drug Discovery / Q000379:methods*; D005047:Etoposide / Q000627:therapeutic use; D005260:Female; D006801:Humans; D020407:Internet; D016239:MEDLINE; D017239:Paclitaxel / Q000627:therapeutic use; D011642:Publications
chemical_list: D000970:Antineoplastic Agents; D005047:Etoposide; D004317:Doxorubicin; D017239:Paclitaxel