title: When drug discovery meets web search: Learning to Rank for ligand-based virtual screening.
abstract: 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 AbstractThe analogy between web search and ligand-based drug discovery.
authors.affiliation: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Huai'an Second People's Hospital affiliated to Xuzhou Medical College, Huai'an, China.
authors.forename: Lijuan
authors.initials: L
authors.lastname: Ji
authors.affiliation: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors.forename: Yanan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors.forename: Kailin
authors.initials: K
authors.lastname: Tang
authors.affiliation: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China ; Department of Computer Science, Hefei University of Technology, Hefei, 230009 China.
authors.forename: Haiping
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors.forename: Ruixin
authors.initials: R
authors.lastname: Zhu
authors.affiliation: R & D Information, AstraZeneca, Shanghai, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Jia
authors.affiliation: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Cao
authors.affiliation: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
keywords: Data integration; Drug discovery; Learning to Rank; Virtual screening