Mining the biomedical literature to predict shared drug targets in DrugBank
Mining the biomedical literature to predict shared drug targets in DrugBank
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DOI：
10.1109/clei.2017.8226376
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Horacio Caniza;Diego Galeano;A. Paccanaro
中科院分区：
文献类型：
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作者：
Horacio Caniza;Diego Galeano;A. Paccanaro
目前的药物开发管道的特点是流程长，磨损率高，成本高。超过80%的新化合物在测试的后期阶段失败，这是由于化合物的未知生物分子靶标引起的严重副作用。在这项工作中，我们提出了一个措施，可以预测药物库中的药物通过大规模的生物医学文献分析共享的目标。我们表明，使用MeSH本体术语可以准确地描述药物，适当使用的MeSH本体结构可以确定成对的药物相似性。
The current drug development pipelines are characterised by long processes with high attrition rates and elevated costs. More than 80% of new compounds fail in the later stages of testing due to severe side-effects caused by unknown biomolecular targets of the compounds. In this work, we present a measure that can predict shared targets for drugs in DrugBank through large scale analysis of the biomedical literature. We show that using MeSH ontology terms can accurately describe the drugs and that appropriate use of the MeSH ontological structure can determine pairwise drug similarity.