When drug discovery meets web search: Learning to Rank for ligand-based virtual screening

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• 作者：Wei Zhang (1)
  Lijuan Ji (2)
  Yanan Chen (1)
  Kailin Tang (1)
  Haiping Wang (1) (4)
  Ruixin Zhu (1)
  Wei Jia (3)
  Zhiwei Cao (1)
  Qi Liu (1)
  
  1. Department of Central Laboratory ; Shanghai Tenth People鈥檚 Hospital ; School of Life Sciences and Technology ; Tongji University ; Shanghai ; China
  2. Huai鈥檃n Second People鈥檚 Hospital affiliated to Xuzhou Medical College ; Huai鈥檃n ; China
  4. Department of Computer Science ; Hefei University of Technology ; Hefei ; 230009 ; China
  3. R & D Information ; AstraZeneca ; Shanghai ; China
  
• 关键词：Learning to Rank ; Virtual screening ; Drug discovery ; Data integration
• 刊名：Journal of Cheminformatics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：7
• 期：1
• 全文大小：2,266 KB
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• 刊物类别：Physics and Astronomy
• 刊物主题：Computer Applications in Chemistry
  Theoretical and Computational Chemistry
  Computational Biology/Bioinformatics
  Documentation and Information in Chemistry
  
• 出版者：Chemistry Central Ltd
• ISSN：1758-2946

文摘

Background 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. Results 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 newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration. Conclusions To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html. Graphical Abstract The analogy between web search and ligand-based drug discovery