Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
J Chem Inf Model. 2018 Feb 26;58(2):212-218. doi: 10.1021/acs.jcim.7b00589. Epub 2018 Jan 19.
Elucidation of the mechanistic relationships between drugs, their targets, and diseases is at the core of modern drug discovery research. Thousands of studies relevant to the drug-target-disease (DTD) triangle have been published and annotated in the Medline/PubMed database. Mining this database affords rapid identification of all published studies that confirm connections between vertices of this triangle or enable new inferences of such connections. To this end, we describe the development of Chemotext, a publicly available Web server that mines the entire compendium of published literature in PubMed annotated by Medline Subject Heading (MeSH) terms. The goal of Chemotext is to identify all known DTD relationships and infer missing links between vertices of the DTD triangle. As a proof-of-concept, we show that Chemotext could be instrumental in generating new drug repurposing hypotheses or annotating clinical outcomes pathways for known drugs. The Chemotext Web server is freely available at http://chemotext.mml.unc.edu .
阐明药物、药物靶点和疾病之间的机制关系是现代药物发现研究的核心。数千项与药物-靶点-疾病(DTD)三角相关的研究已在 Medline/PubMed 数据库中发表和注释。挖掘该数据库可以快速识别所有已发表的研究,这些研究证实了该三角顶点之间的联系,或能够对这些联系进行新的推断。为此,我们描述了 Chemotext 的开发,这是一个公开可用的 Web 服务器,它可以挖掘 Medline 主题词(MeSH)术语注释的 PubMed 中所有已发表文献的全文。Chemotext 的目标是识别所有已知的 DTD 关系,并推断 DTD 三角顶点之间缺失的联系。作为概念验证,我们表明 Chemotext 可以帮助生成新的药物再利用假说,或注释已知药物的临床结果途径。Chemotext Web 服务器可免费在 http://chemotext.mml.unc.edu 获得。