Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review
作者: Tiejun ChengMing HaoTakako TakedaStephen H. BryantYanli Wang
作者单位: 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health
刊名: The AAPS Journal, 2017, Vol.19 (5), pp.1264-1275
来源数据库: Springer Journal
DOI: 10.1208/s12248-017-0092-6
关键词: Compound-protein interactionsDrug repositioningDrug-target interactionsPublic databases
英文摘要: The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We...
原始语种摘要: The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We...
全文获取路径: Springer  (合作)
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影响因子:4.386 (2012)

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关键词翻译
关键词翻译
  • 就业 普及
  • computational 计算的
  • Data 数据
  • integration 集成
  • popular 普及
  • employed 普及
  • scale 度盘
  • large 大的
  • growing 饲育
  • remain 
  • prediction 预报