When drug discovery meets web search: Learning to Rank for ligand-based virtual screening
作者: Wei ZhangLijuan JiYanan ChenKailin TangHaiping WangRuixin ZhuWei JiaZhiwei CaoQi Liu
作者单位: 1Department of Central Laboratory, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University
2Huai’an Second People’s Hospital affiliated to Xuzhou Medical College
3R & D Information, AstraZeneca
4Department of Computer Science, Hefei University of Technology
刊名: Journal of Cheminformatics, 2015, Vol.7 (1), pp.1-13
来源数据库: Springer Nature Journal
DOI: 10.1186/s13321-015-0052-z
关键词: Learning to RankVirtual screeningDrug discoveryData integration
英文摘要: Abstract(#br) Background(#br)The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms.(#br) Results(#br)A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the...
全文获取路径: Springer Nature  (合作)
影响因子:3.59 (2012)

  • virtual 虚的
  • discovery 发现
  • web 梁腹板
  • screening 筛分
  • based 基于
  • search 搜索
  • ligand 配合体